Search results for: logistics network optimization

Authors: A. Moualdia, D. J. Boudana, O. Bouchhida, A. Medjber

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Wind energy has many advantages, it does not pollute and it is an inexhaustible source. However, the cost of this energy is still too high to compete with traditional fossil fuels, especially on sites less windy. The performance of a wind turbine depends on three parameters: the power of wind, the power curve of the turbine and the generator's ability to respond to wind fluctuations. This paper presents a control chain conversion based on a double-fed asynchronous machine and flow-oriented. The supply system comprises of two identical converters, one connected to the rotor and the other one connected to the network via a filter. The architecture of the device is up by three commands are necessary for the operation of the turbine control extraction of maximum power of the wind to control itself (MPPT) control of the rotor side converter controlling the electromagnetic torque and stator reactive power and control of the grid side converter by controlling the DC bus voltage and active power and reactive power exchanged with the network. The proposed control has been validated in both modes of operation of the three-bladed wind 7.5 kW, using Matlab/Simulink. The results of simulation control technology study provide good dynamic performance and static.

Keywords: D.F.I.G, variable wind speed, hypersynchrone, energy quality, hyposynchrone

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Authors: Zbigniew Czyz, Ksenia Siadkowska, Krzysztof Skiba, Karol Scislowski

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Today’s progress in the rotorcraft is mostly associated with an optimization of aircraft performance achieved by active and passive modifications of main rotor assemblies and a tail propeller. The key task is to improve their performance, improve the hover quality factor for rotors but not change in specific fuel consumption. One of the tasks to improve the helicopter is an active optimization of the main rotor providing for flight stages, i.e., an ascend, flight, a descend. An active interference with the airflow around the rotor blade section can significantly change characteristics of the aerodynamic airfoil. The efficiency of actuator systems modifying aerodynamic coefficients in the current solutions is relatively high and significantly affects the increase in strength. The solution to actively change aerodynamic characteristics assumes a periodic change of geometric features of blades depending on flight stages. Changing geometric parameters of blade warping enables an optimization of main rotor performance depending on helicopter flight stages. Structurally, an adaptation of shape memory alloys does not significantly affect rotor blade fatigue strength, which contributes to reduce costs associated with an adaptation of the system to the existing blades, and gains from a better performance can easily amortize such a modification and improve profitability of such a structure. In order to obtain quantitative and qualitative data to solve this research problem, a number of numerical analyses have been necessary. The main problem is a selection of design parameters of the main rotor and a preliminary optimization of its performance to improve the hover quality factor for rotors. This design concept assumes a three-bladed main rotor with a chord of 0.07 m and radius R = 1 m. The value of rotor speed is a calculated parameter of an optimization function. To specify the initial distribution of geometric warping, a special software has been created that uses a numerical method of a blade element which respects dynamic design features such as fluctuations of a blade in its joints. A number of performance analyses as a function of rotor speed, forward speed, and altitude have been performed. The calculations were carried out for the full model assembly. This approach makes it possible to observe the behavior of components and their mutual interaction resulting from the forces. The key element of each rotor is the shaft, hub and pins holding the joints and blade yokes. These components are exposed to the highest loads. As a result of the analysis, the safety factor was determined at the level of k > 1.5, which gives grounds to obtain certification for the strength of the structure. The construction of the joint rotor has numerous moving elements in its structure. Despite the high safety factor, the places with the highest stresses, where the signs of wear and tear may appear, have been indicated. The numerical analysis carried out showed that the most loaded element is the pin connecting the modular bearing of the blade yoke with the element of the horizontal oscillation joint. The stresses in this element result in a safety factor of k=1.7. The other analysed rotor components have a safety factor of more than 2 and in the case of the shaft, this factor is more than 3. However, it must be remembered that the structure is as strong as the weakest cell is. Designed rotor for unmanned aerial vehicles adapted to work with blades with intelligent materials in its structure meets the requirements for certification testing. Acknowledgement: This work has been financed by the Polish National Centre for Research and Development under the LIDER program, Grant Agreement No. LIDER/45/0177/L-9/17/NCBR/2018.

Keywords: main rotor, rotorcraft aerodynamics, shape memory alloy, materials, unmanned helicopter

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Authors: Mouli Banga, Chaya Ravindra

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Wound classification is a crucial step in wound diagnosis. An effective classifier can aid wound specialists in identifying wound types with reduced financial and time investments, facilitating the determination of optimal treatment procedures. This study presents a deep neural network-based classifier that leverages wound images and their corresponding locations to categorize wounds into various classes, such as diabetic, pressure, surgical, and venous ulcers. By incorporating a developed body map, the process of tagging wound locations is significantly enhanced, providing healthcare specialists with a more efficient tool for wound analysis. We conducted a comparative analysis between two prominent convolutional neural network models, ResNet50 and MobileNetV2, utilizing a dataset of 730 images. Our findings reveal that the RestNet50 outperforms MovileNetV2, achieving an accuracy of approximately 90%, compared to MobileNetV2’s 83%. This disparity highlights the superior capability of ResNet50 in the context of this dataset. The results underscore the potential of integrating deep learning with spatial data to improve the precision and efficiency of wound diagnosis, ultimately contributing to better patient outcomes and reducing healthcare costs.

Keywords: wound classification, MobileNetV2, ResNet50, multimodel

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Authors: Christofas Stergianos, Jason Atkin, Herve Morvan

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As air traffic increases, more airports are interested in utilizing optimization methods. Many processes happen in parallel at an airport, and complex models are needed in order to have a reliable solution that can be implemented for ground movement operations. The ground movement for aircraft in an airport, allocating a path to each aircraft to follow in order to reach their destination (e.g. runway or gate), is one process that could be optimized. The Quickest Path Problem with Time Windows (QPPTW) algorithm has been developed to provide a conflict-free routing of vehicles and has been applied to routing aircraft around an airport. It was subsequently modified to increase the accuracy for airport applications. These modifications take into consideration specific characteristics of the problem, such as: the pushback process, which considers the extra time that is needed for pushing back an aircraft and turning its engines on; stand holding where any waiting should be allocated to the stand; and runway sequencing, where the sequence of the aircraft that take off is optimized and has to be respected. QPPTW involves searching for the quickest path by expanding the search in all directions, similarly to Dijkstra’s algorithm. Finding a way to direct the expansion can potentially assist the search and achieve a better performance. We have further modified the QPPTW algorithm to use a heuristic approach in order to guide the search. This new algorithm is based on the A-star search method but estimates the remaining time (instead of distance) in order to assess how far the target is. It is important to consider the remaining time that it is needed to reach the target, so that delays that are caused by other aircraft can be part of the optimization method. All of the other characteristics are still considered and time windows are still used in order to route multiple aircraft rather than a single aircraft. In this way the quickest path is found for each aircraft while taking into account the movements of the previously routed aircraft. After running experiments using a week of real aircraft data from Zurich Airport, the new algorithm (A-star QPPTW) was found to route aircraft much more quickly, being especially fast in routing the departing aircraft where pushback delays are significant. On average A-star QPPTW could route a full day (755 to 837 aircraft movements) 56% faster than the original algorithm. In total the routing of a full week of aircraft took only 12 seconds with the new algorithm, 15 seconds faster than the original algorithm. For real time application, the algorithm needs to be very fast, and this speed increase will allow us to add additional features and complexity, allowing further integration with other processes in airports and leading to more optimized and environmentally friendly airports.

Keywords: a-star search, airport operations, ground movement optimization, routing and scheduling

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Authors: Ashagrie Getnet Flattie

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Line-of-sight (LOS) information, data rates, good quality, and flexible network service are limited by the fact that, for the duration of any given connection, they experience severe variation in signal strength due to fading and path loss. Wireless system faces major challenges in achieving wide coverage and capacity without affecting the system performance and to access data everywhere, all the time. In this paper, the cell coverage and edge rate of different Multiple-input multiple-output (MIMO) schemes in 20 MHz Long Term Evolution (LTE) system under Unmanned Air Vehicles (UAV) platform are investigated. After some background on the enormous potential of UAV, MIMO, and LTE in wireless links, the paper highlights the presented system model which attempts to realize the various benefits of MIMO being incorporated into UAV platform. The performances of the three MIMO LTE schemes are compared with the performance of 4x4 MIMO LTE in UAV scheme carried out to evaluate the improvement in cell radius, BER, and data throughput of the system in different morphology. The results show that significant performance gains such as bit error rate (BER), data rate, and coverage can be achieved by using the presented scenario.

Keywords: LTE, MIMO, path loss, UAV

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Authors: Yating Yang, Xue Zhang, Chengli Zhao

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Technology capability refers to the comprehensive ability that influences all factors of technological development. Among them, researchers’ resources serve as the foundation and driving force for technology capability, representing a significant manifestation of a country/region's technological capability. Therefore, the cross-border transfer behavior of researchers to some extent reflects changes in technological capability between countries/regions, providing a unique research perspective for technological capability assessment. This paper proposes a technological capability assessment model based on personnel transfer networks, which consists of a researchers' transfer network model and a country/region role evolution model. It evaluates the changes in a country/region's technological capability roles from the perspective of researcher transfers and conducts an analysis using artificial intelligence as a case study based on literature data. The study reveals that the United States, China, and the European Union are core nodes, and identifies the role evolution characteristics of several major countries/regions.

Keywords: transfer network, technological capability assessment, central-peripheral structure, role evolution

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Authors: Yonas Shuke Kitawa

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Background: Although malaria incidence has substantially fallen sharply over the past few years, the rate of decline varies by district, time, and malaria type. Despite this turn-down, malaria remains a major public health threat in various districts of Ethiopia. Consequently, the present study is aimed at developing a predictive model that helps to identify the spatio-temporal variation in malaria risk by multiple plasmodium species. Methods: We propose a multivariate spatio-temporal Bayesian model to obtain a more coherent picture of the temporally varying spatial variation in disease risk. The spatial autocorrelation in such a data set is typically modeled by a set of random effects that assign a conditional autoregressive prior distribution. However, the autocorrelation considered in such cases depends on a binary neighborhood matrix specified through the border-sharing rule. Over here, we propose a graph-based optimization algorithm for estimating the neighborhood matrix that merely represents the spatial correlation by exploring the areal units as the vertices of a graph and the neighbor relations as the series of edges. Furthermore, we used aggregated malaria count in southern Ethiopia from August 2013 to May 2019. Results: We recognized that precipitation, temperature, and humidity are positively associated with the malaria threat in the area. On the other hand, enhanced vegetation index, nighttime light (NTL), and distance from coastal areas are negatively associated. Moreover, nonlinear relationships were observed between malaria incidence and precipitation, temperature, and NTL. Additionally, lagged effects of temperature and humidity have a significant effect on malaria risk by either species. More elevated risk of P. falciparum was observed following the rainy season, and unstable transmission of P. vivax was observed in the area. Finally, P. vivax risks are less sensitive to environmental factors than those of P. falciparum. Conclusion: The improved inference was gained by employing the proposed approach in comparison to the commonly used border-sharing rule. Additionally, different covariates are identified, including delayed effects, and elevated risks of either of the cases were observed in districts found in the central and western regions. As malaria transmission operates in a spatially continuous manner, a spatially continuous model should be employed when it is computationally feasible.

Keywords: disease mapping, MSTCAR, graph-based optimization algorithm, P. falciparum, P. vivax, waiting matrix

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Authors: Md Mizanur Rahman, M. Aslam Alam, Muhammad Abu Yusuf

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The abundance of Jellyfish across the coasts of the Bay of Bengal is increasing sharply due to marine pollution, increased sea acidification and climate change. Jellyfish draws our attention to address the local and global stressors. This also indicates that something wrong is happening in this bay behind the scenes. This study aimed to investigate how the policy framework governing the sea can be reformed. To do so, this study evaluated the existing policy, regulatory and institutional framework. Empirical data were collected from the middle coastal zone of Bangladesh. The secondary literature on policy, legal documents, and institutional arrangements were reviewed. The causes of poor coordination among different public sectors and non-compliance of laws were identified. The key findings show that despite the existing of Department of Environment, poor coordination with other departments, and lack of logistics and technical staffs have resulted in severe marine pollution and degradation of coastal and marine living resources. The existing policies had no monitoring and evaluation mechanisms. Non-compliance of the existing laws has been fueling the problems. This study provides an integrated policy and a guideline for updating the legal and institutional mechanism to manage coastal and marine living resources sustainably in Bangladesh to achieve Sustainable Development Goal 14.

Keywords: legal, institutional, framework, jellyfish

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Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

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Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

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Authors: Andres Zuñiga Orozco

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Green manure fertilizers have special importance in the development of organic and sustainable agriculture as a substitute or complement to chemical fertilization. They have many advantages, but they have application limitations in greenhouse crops and in open field crops that have low growing size. On the other hand, the logistics of sowing, harvesting and applying have been difficult for producers to adopt. For this reason, a pelletized presentation was designed in conjunction with Trichoderma harzianum. The biopellet was applied in pineapple as the first experience, managing to improve carbon levels in the soil and some nutrients. Then it was applied to tomatoes where it was proven that, nutritionally, it is possible to nourish the crop up to day 60 only with the biopellet, improve carbon levels in soil and control the fungus Fusarium oxysporum. Subsequently, it was applied to coffee seedlings with an organo-mineral formulation. Here, the improvement in the growth and nutrition of the plants was notable, as well as the increase in the microbial activity of the soil. M. pruriens biopellets allow crops to be nourished, allow biocontrolers to be added, improve soil conditions to promote greater microbial activity, reincorporate carbon and CO2 into the soil, are easily applicable, allow dosing and have a favorable shelf-life. They can be applied to all types of crops, both in the greenhouse and in the field.

Keywords: Mucuna pruriens, pellets, carbon, Trichoderma, Fusarium

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Authors: R. Ramesh, K. K. Shivaraman

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The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

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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: Dyah Titisari Widyastuti

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Rail-station area development that is based on the concept of TOD (Transit Oriented Development) is principally oriented to pedestrian accessibility for daily mobility. The aim of this research is elaborating how far the existing physical elements of a rail-station district could facilitate pedestrian mobility and establish a pedestrian friendly district toward implementation of a TOD concept. This research was conducted through some steps: (i) mapping the rail-station area pedestrian sidewalk and pedestrian network as well as activity nodes and transit nodes, (ii) assessing the level of pedestrian sidewalk connectivity joining trip origin and destination. The research area coverage in this case is limited to walking distance of the rail station (around 500 meters or 10-15 minutes walking). The findings of this research on the current condition of the street and pedestrian sidewalk network and connectivity, show good preference for the foot modal share (more than 50%) is achieved. Nevertheless, it depends on the distance from the trip origin to destination.

Keywords: accessibility of daily mobility, pedestrian-friendly district, rail-station district, transit oriented development

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Authors: Wann-Ming Wey

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In recent years, in addition to face the external threats such as energy shortages and climate change, traffic congestion and environmental pollution have become anxious problems for many cities. Considering private automobile-oriented urban development had produced many negative environmental and social impacts, the transit-oriented development (TOD) has been considered as a sustainable urban model. TOD encourages public transport combined with friendly walking and cycling environment designs, however, non-motorized modes help improving human health, energy saving, and reducing carbon emissions. Due to environmental changes often affect the planners’ decision-making; this research applies dynamic network process (DNP) which includes the time dependent concept to promoting friendly walking and cycling environmental designs as an advanced planning support system for environment improvements. This research aims to discuss what kinds of design strategies can improve a friendly walking and cycling environment under TOD. First of all, we collate and analyze environment designing factors by reviewing the relevant literatures as well as divide into three aspects of “safety”, “convenience”, and “amenity” from fifteen environment designing factors. Furthermore, we utilize fuzzy Delphi Technique (FDT) expert questionnaire to filter out the more important designing criteria for the study case. Finally, we utilized DNP expert questionnaire to obtain the weights changes at different time points for each design criterion. Based on the changing trends of each criterion weight, we are able to develop appropriate designing strategies as the reference for planners to allocate resources in a dynamic environment. In order to illustrate the approach we propose in this research, Taipei city as one example has been used as an empirical study, and the results are in depth analyzed to explain the application of our proposed approach.

Keywords: environment planning support systems, walking and cycling, transit-oriented development (TOD), dynamic network process (DNP)

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Authors: Ayu Nabila Kusuma Pradana, Aprinaldi Jasa Mantau, Tomohiko Akahoshi

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The incidence of Disseminated intravascular coagulation (DIC) following gastrointestinal surgery has a poor prognosis. Therefore, it is important to determine the factors that can predict the prognosis of DIC. This study will investigate the factors that may influence the outcome of DIC in patients after gastrointestinal surgery. Eighty-one patients were admitted to the intensive care unit after gastrointestinal surgery in Kyushu University Hospital from 2003 to 2021. Acute DIC scores were estimated using the new Japanese Association for Acute Medicine (JAAM) score from before and after surgery from day 1, day 3, and day 7. Acute DIC scores will be compared with The Sequential Organ Failure Assessment (SOFA) score, platelet count, lactate level, and a variety of biochemical parameters. This study applied machine learning algorithms to predict the prognosis of DIC after gastrointestinal surgery. The results of this study are expected to be used as an indicator for evaluating patient prognosis so that it can increase life expectancy and reduce mortality from cases of DIC patients after gastrointestinal surgery.

Keywords: the survival rate, gastrointestinal surgery, JAAM score, neural network, machine learning, disseminated intravascular coagulation (DIC)

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Authors: Jin Woo Hwang, Byung Kwan Oh, Yousok Kim, Hyo Seon Park

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As greenhouse effect has been recognized as serious environmental problem of the world, interests in carbon dioxide (CO₂) emission which comprises major part of greenhouse gas (GHG) emissions have been increased recently. Since construction industry takes a relatively large portion of total CO₂ emissions of the world, extensive studies about reducing CO₂ emissions in construction and operation of building have been carried out after the 2000s. Also, performance based design (PBD) methodology based on nonlinear analysis has been robustly developed after Northridge Earthquake in 1994 to assure and assess seismic performance of building more exactly because structural engineers recognized that prescriptive code based design approach cannot address inelastic earthquake responses directly and assure performance of building exactly. Although CO₂ emissions and PBD approach are recent rising issues on construction industry and structural engineering, there were few or no researches considering these two issues simultaneously. Thus, the objective of this study is to minimize the CO₂ emissions and cost of building designed by PBD approach in structural design stage considering structural materials. 4 story and 4 span reinforced concrete building optimally designed to minimize CO₂ emissions and cost of building and to satisfy specific seismic performance (collapse prevention in maximum considered earthquake) of building satisfying prescriptive code regulations using non-dominated sorting genetic algorithm-II (NSGA-II). Optimized design result showed that minimized CO₂ emissions and cost of building were acquired satisfying specific seismic performance. Therefore, the methodology proposed in this paper can be used to reduce both CO₂ emissions and cost of building designed by PBD approach.

Keywords: CO2 emissions, performance based design, optimization, sustainable design

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Authors: Siddhartha Chauhan, Nitin Kumar Kotania

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Traditional Wireless Sensor Networks (WSNs) generally use static sinks to collect data from the sensor nodes via multiple forwarding. Therefore, network suffers with some problems like long message relay time, bottle neck problem which reduces the performance of the network. Many approaches have been proposed to prevent this problem with the help of mobile sink to collect the data from the sensor nodes, but these approaches still suffer from the buffer overflow problem due to limited memory size of sensor nodes. This paper proposes an energy efficient scheme for data gathering which overcomes the buffer overflow problem. The proposed scheme creates virtual grid structure of heterogeneous nodes. Scheme has been designed for sensor nodes having variable sensing rate. Every node finds out its buffer overflow time and on the basis of this cluster heads are elected. A controlled traversing approach is used by the proposed scheme in order to transmit data to sink. The effectiveness of the proposed scheme is verified by simulation.

Keywords: buffer overflow problem, mobile sink, virtual grid, wireless sensor networks

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Authors: Qikai Guan

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With the deepening of China's urbanization process, the development of urban industry has entered a new period, due to the gradual compounding and diversification of urban industrial functions, urban planning has shifted from the previous single industrial space arrangement and functional design to focusing on the upgrading of the urban structure, and on the diversified needs of people. As an important part of urban activity space, ‘slow moving space’ is of great significance in alleviating urban traffic congestion, optimizing residents' travel experience and improving urban ecological space. Therefore, this paper takes the slow-moving transportation system under the perspective of industry-city integration as the starting point, through sorting out the development needs of the city in the process of industry-city integration, analyzing the characteristics of the site base, sorting out a series of compatibility between the layout of the new industrial zone and the urban slow-moving system, and integrating the design concepts. At the same time, through the analysis and summarization of domestic and international experience, the construction ideas are proposed. Finally, the following aspects of planning strategy optimization are proposed: industrial layout, urban vitality, ecological pattern, regional characteristics and landscape image. In terms of specific design, on the one hand, it builds a regional slow-moving network, puts forward a diversified design strategy for the industry-oriented and multi-functional composite central area, realizes the coexistence of pedestrian-oriented and multiple transportation modes, basically covers the public facilities, and enhances the vitality of the city. On the other hand, it improves the landscape ecosystem, creates a healthy, diversified and livable superline landscape system, helps the construction of the ‘green core’ of the central city, and improves the travel experience of the residents.

Keywords: industry-city integration, slow-moving system, public space, functional integration

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Authors: Chaudhary Muhammad Aqdus Ilyas, Matthias Rehm, Kamal Nasrollahi, Thomas B. Moeslund

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This paper presents the investigation of data collection procedures, associated with robots when placed with traumatic brain injured (TBI) patients for rehabilitation purposes through facial expression and mood analysis. Rehabilitation after TBI is very crucial due to nature of injury and variation in recovery time. It is advantageous to analyze these emotional signals in a contactless manner, due to the non-supportive behavior of patients, limited muscle movements and increase in negative emotional expressions. This work aims at the development of framework where robots can recognize TBI emotions through facial expressions to perform rehabilitation tasks by physical, cognitive or interactive activities. The result of these studies shows that with customized data collection strategies, proposed framework identify facial and emotional expressions more accurately that can be utilized in enhancing recovery treatment and social interaction in robotic context.

Keywords: computer vision, convolution neural network- long short term memory network (CNN-LSTM), facial expression and mood recognition, multimodal (RGB-thermal) analysis, rehabilitation, robots, traumatic brain injured patients

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Authors: Constanza A. Barriga, Rafael Bernardi, Amokrane Berdja, Christian D. Guzman

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Most image restoration methods in astronomy rely upon probabilistic tools that infer the best solution for a deconvolution problem. They achieve good performances when the point spread function (PSF) is spatially invariable in the image plane. However, this latter condition is not always satisfied with real optical systems. PSF angular variations cannot be evaluated directly from the observations, neither be corrected at a pixel resolution. We have developed a method for the restoration of images affected by static and anisotropic aberrations using deep neural networks that can be directly applied to sky images. The network is trained using simulated sky images corresponding to the T-80 telescope optical system, an 80 cm survey imager at Cerro Tololo (Chile), which are synthesized using a Zernike polynomial representation of the optical system. Once trained, the network can be used directly on sky images, outputting a corrected version of the image, which has a constant and known PSF across its field-of-view. The method was tested with the T-80 telescope, achieving better results than with PSF deconvolution techniques. We present the method and results on this telescope.

Keywords: aberrations, deep neural networks, image restoration, variable point spread function, wide field images

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Authors: Odile Amoncou, Djedje-Kossu Zahui

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The increased demand for digital communications during the COVID-19 pandemic has seen an unprecedented surge in new telecom infrastructure around the world. The expansion has been more remarkable in countries with developing telecom infrastructures. Particularly, the three telecom operators in Cote d’Ivoire, Orange CI, MTN CI, and MOOV Africa, have considerably scaled up their exploitation technologies and capacities in terms of towers, fiber optic installation, and customer service hubs. The trend will likely continue upward while expanding the carbon footprint of the Ivorian telecom operators. Therefore, the corporate social and environmental responsibilities of these telecommunication companies can no longer be overlooked. This paper assesses the sustainability of the three Ivorian telecommunication network operators by applying a combination of commonly used sustainability management indexes. These tools are streamlined and adapted to the relatively young and developing digital network of Cote D’Ivoire. We trust that this article will push the respective CEOs to make sustainability a top strategic priority and understand the substantial potential returns in terms of saving, new products, and new clients while improving their corporate image. In addition, good sustainability management can increase their stakeholders.

Keywords: sustainability of telecom operators, sustainability management index, carbon footprint, digital communications

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Authors: Shahrukh Ahmad, Purnendu Bose

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Algal-Bacterial photobioreactors (ABPBRs) have emerged as a promising technology for sustainable biomass production and wastewater treatment. Nutrient removal is seldom done in sewage treatment plants and large volumes of wastewater which still have nutrients are being discharged and that can lead to eutrophication. That is why ABPBR plays a vital role in wastewater treatment. However, improving the efficiency of ABPBR remains a significant challenge. This study aims to enhance ABPBR efficiency by focusing on two key aspects: nutrient removal and cost-effective optimization of the light source. By integrating nutrient removal and cost analysis for light source optimization, this study proposes practical strategies for improving ABPBR efficiency. To reduce organic carbon and convert ammonia to nitrates, domestic wastewater from a 130 MLD sewage treatment plant (STP) was aerated with a hydraulic retention time (HRT) of 2 days. The treated supernatant had an approximate nitrate and phosphate values of 16 ppm as N and 6 ppm as P, respectively. This supernatant was then fed into the ABPBR, and the removal of nutrients (nitrate as N and phosphate as P) was observed using different colored LED bulbs, namely white, blue, red, yellow, and green. The ABPBR operated with a 9-hour light and 3-hour dark cycle, using only one color of bulbs per cycle. The study found that the white LED bulb, with a photosynthetic photon flux density (PPFD) value of 82.61 µmol.m-2 .sec-1 , exhibited the highest removal efficiency. It achieved a removal rate of 91.56% for nitrate and 86.44% for phosphate, surpassing the other colored bulbs. Conversely, the green LED bulbs showed the lowest removal efficiencies, with 58.08% for nitrate and 47.48% for phosphate at an HRT of 5 days. The quantum PAR (Photosynthetic Active Radiation) meter measured the photosynthetic photon flux density for each colored bulb setting inside the photo chamber, confirming that white LED bulbs operated at a wider wavelength band than the others. Furthermore, a cost comparison was conducted for each colored bulb setting. The study revealed that the white LED bulb had the lowest average cost (Indian Rupee)/light intensity (µmol.m-2 .sec-1 ) value at 19.40, while the green LED bulbs had the highest average cost (INR)/light intensity (µmol.m-2 .sec-1 ) value at 115.11. Based on these comparative tests, it was concluded that the white LED bulbs were the most efficient and costeffective light source for an algal photobioreactor. They can be effectively utilized for nutrient removal from secondary treated wastewater which helps in improving the overall wastewater quality before it is discharged back into the environment.

Keywords: algal bacterial photobioreactor, domestic wastewater, nutrient removal, led bulbs

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Authors: Johannes Günther

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Time Sensitive Networking (TSN), standardized in the IEEE 802.1 standard, has been lent increasing attention in the context of mission critical systems. Such mission critical systems, e.g., in the automotive domain, aviation, industrial, and smart factory domain, are responsible for coordinating complex functionalities in real time. In many of these contexts, a reliable data exchange fulfilling hard time constraints and quality of service (QoS) conditions is of critical importance. TSN standards are able to provide guarantees for deterministic communication behaviour, which is in contrast to common best-effort approaches. Therefore, the superior QoS guarantees of TSN may aid in the development of new technologies, which rely on low latencies and specific bandwidth demands being fulfilled. TSN extends existing Ethernet protocols with numerous standards, providing means for synchronization, management, and overall real-time focussed capabilities. These additional QoS guarantees, as well as management mechanisms, lead to an increased attack surface for potential malicious attackers. As TSN guarantees certain deadlines for priority traffic, an attacker may degrade the QoS by delaying a packet beyond its deadline or even execute a denial of service (DoS) attack if the delays lead to packets being dropped. However, thus far, security concerns have not played a major role in the design of such standards. Thus, while TSN does provide valuable additional characteristics to existing common Ethernet protocols, it leads to new attack vectors on networks and allows for a range of potential attacks. One answer to these security risks is to deploy defense mechanisms according to a moving target defense (MTD) strategy. The core idea relies on the reduction of the attackers' knowledge about the network. Typically, mission-critical systems suffer from an asymmetric disadvantage. DoS or QoS-degradation attacks may be preceded by long periods of reconnaissance, during which the attacker may learn about the network topology, its characteristics, traffic patterns, priorities, bandwidth demands, periodic characteristics on links and switches, and so on. Here, we implemented and tested several MTD-like defense strategies against different attacker models of varying capabilities and budgets, as well as collaborative attacks of multiple attackers within a network, all within the context of TSN networks. We modelled the networks and tested our defense strategies on an OMNET++ testbench, with networks of different sizes and topologies, ranging from a couple dozen hosts and switches to significantly larger set-ups.

Keywords: network security, time sensitive networking, moving target defense, cyber security

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Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

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Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

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Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan

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Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.

Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network

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Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

Procedia PDF Downloads 391

Authors: Apurva Patil, Ashay Aswale, Akshay Kulkarni, Shubham Bharadiya

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The potential benefit of gravity compensation of linkages in mechanisms using springs to reduce actuator requirements is well recognized, but practical applications have been elusive. Although existing methods provide exact spring balance, they require additional masses or auxiliary links, or all the springs used originate from the ground, which makes the resulting device bulky and space-inefficient. This paper uses a method of static balancing of mechanisms with conservative loads such as gravity and spring loads using non-zero-free-length springs with child–parent connections and no auxiliary links. Application of this method to the developed arm of a humanoid robot is presented here. Spring balancing is particularly important in this case because the serial chain of linkages has to work against gravity.This work involves approximate spring balancing of the open-loop chain of linkages using minimization of potential energy variance. It uses the approach of flattening the potential energy distribution over the workspace and fuses it with numerical optimization. The results show the considerable reduction in actuator torque requirement with practical spring design and arrangement. Reduced actuator torque facilitates the use of lower end actuators which are generally smaller in weight and volume thereby lowering the space requirements and the total weight of the arm. This is particularly important for humanoid robots where the parent actuator has to handle the weight of the subsequent actuators as well. Actuators with lower actuation requirements are more energy efficient, thereby reduce the energy consumption of the mechanism. Lower end actuators are lower in cost and facilitate the development of low-cost devices. Although the method provides only an approximate balancing, it is versatile, flexible in choosing appropriate control variables that are relevant to the design problem and easy to implement. The true potential of this technique lies in the fact that it uses a very simple optimization to find the spring constant, free-length of the spring and the optimal attachment points subject to the optimization constraints. Also, it uses physically realizable non-zero-free-length springs directly, thereby reducing the complexity involved in simulating zero-free-length springs from non-zero-free-length springs. This method allows springs to be attached to the preceding parent link, which makes the implementation of spring balancing practical. Because auxiliary linkages can be avoided, the resultant arm of the humanoid robot is compact. The cost benefits and reduced complexity can be significant advantages in the development of this arm of the humanoid robot.

Keywords: actuator torque, child-parent connections, spring balancing, the arm of a humanoid robot

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Authors: Hassam Hassan Elborombaly

Abstract:

The pollution generated from transportation modes, congestion and traffic heritage has led to the deterioration of historic buildings and the urban heritage in historic cities. Accordingly, this paper attempts to diagnose the transport and traffic problems in historic cities. In general and in Heritage Cities, and to investigate methods for conserving the urban heritage from negative effects of traffic congestion and of the traditional red modes of transportation. It also attempts to explore possible areas for intervention to mitigate transportation and traffic problems in the light of the principles of the sustainable transportation framework. It aims to draw conclusion and propose recommendation that would increase the efficiency and effectiveness of transportation plans in historic Cairo and consequently achieve sustainable transportation. Problems In historic cities public paths compose an irregular network enclosing large residential plots (defined as super blocks quarters or hettas). The blocks represent the basic morphology units in historic Cities. Each super block incorporates several uses (i.e. residential, non-residential, service uses and others). Local paths reach the interior of the super blocks in an organized inter core, which deals mainly with residential functions mixed with handicraft activities and is composed of several local path units; (b) the other core, which is bound by the public paths and contains a combination of residential, commercial and social activities. Objectives: 1- To provide amenity convenience and comfort for visitors and people who live and work in the area. Pedestrianizing, accessibility and safety are to be reinforced while respecting the organic urban pattern. 2- To enhance street life, vitality and activity, in order to attract people and increase economic prosperity. Research Contents • Relation between residential areas and transportation in the inner core • Analytical studies for historic areas in heritage cities • Sustainable transportation planning in heritage cities • Dynamic and flexible methodology for achieving sustainable transportation network for the Heritage Cities • Result and Recommendation

Keywords: irregular network, public paths, sustainable transportation, urban heritage

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Authors: Ş. Karabulut, A. Güllü, A. Güldaş, R. Gürbüz

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This study investigates the effects of the lead angle and chip thickness variation on surface roughness during the machining of compacted graphite iron using ceramic cutting tools under dry cutting conditions. Analytical models were developed for predicting the surface roughness values of the specimens after the face milling process. Experimental data was collected and imported to the artificial neural network model. A multilayer perceptron model was used with the back propagation algorithm employing the input parameters of lead angle, cutting speed and feed rate in connection with chip thickness. Furthermore, analysis of variance was employed to determine the effects of the cutting parameters on surface roughness. Artificial neural network and regression analysis were used to predict surface roughness. The values thus predicted were compared with the collected experimental data, and the corresponding percentage error was computed. Analysis results revealed that the lead angle is the dominant factor affecting surface roughness. Experimental results indicated an improvement in the surface roughness value with decreasing lead angle value from 88° to 45°.

Keywords: CGI, milling, surface roughness, ANN, regression, modeling, analysis

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Authors: Kuang-Hsiung Tan, Chih-Chan Hu, Chien-Wu Lan, Shih-Sung Lin, Te-Jen Chang

Abstract:

An active islanding detection method using disturbance signal injection with intelligent controller is proposed in this study. First, a DC\AC power inverter is emulated in the distributed generator (DG) system to implement the tracking control of active power, reactive power outputs and the islanding detection. The proposed active islanding detection method is based on injecting a disturbance signal into the power inverter system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the utility power is disconnected. Moreover, in order to improve the transient and steady-state responses of the active power and reactive power outputs of the power inverter, and to further improve the performance of the islanding detection method, two probabilistic fuzzy neural networks (PFNN) are adopted to replace the traditional proportional-integral (PI) controllers for the tracking control and the islanding detection. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the tracking control and the proposed active islanding detection method are verified with experimental results.

Keywords: distributed generators, probabilistic fuzzy neural network, islanding detection, non-detection zone

Procedia PDF Downloads 389