Search results for: cloud computing framework

Authors: Ahmed I. Alutabi, Naghmeh Dezhabad, Sudhakar Ganti

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Data centers have been growing in size and dema nd continuously in the last two decades. Planning for the deployment of resources has been shallow and always resorted to over-provisioning. Data center operators try to maximize the availability of their services by allocating multiple of the needed resources. One resource that has been wasted, with little thought, has been energy. In recent years, programmable resource allocation has paved the way to allow for more efficient and robust data centers. In this work, we examine the predictability of resource usage in a data center environment. We use a number of models that cover a wide spectrum of machine learning categories. Then we establish a framework to guarantee the client service level agreement (SLA). Our results show that using prediction can cut energy loss by up to 55%.

Keywords: machine learning, artificial intelligence, prediction, data center, resource allocation, green computing

Procedia PDF Downloads 108

Authors: Ahmet Kayabasi, Ali Akdagli

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In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.

Keywords: a-shaped compact microstrip antenna, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM)

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Authors: Prosper Tafadzwa Denhere, Ephias Ruhode, Munyaradzi Zhou

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Health Information Systems (HISs) interoperability is emerging to be the future of modern healthcare systems Enterprise Architecture (EA), where healthcare entities are seamlessly interconnected to share healthcare data. The reality that the healthcare industry has been characterised by an influx of fragmented stand-alone e-Health systems, which present challenges of healthcare information sharing across platforms, desires much attention for systems integration efforts. The lack of an EA conceptual framework resultantly crates the need for investigating an ideal solution to the objective of Health Information Systems interoperability development assessment. The study takes a qualitative exploratory approach through a design science research context. The research aims to study the various themes withdrawn from the literature that can help in the assessment of interoperable HISs development through a literature study. Themes derived from the study include HIS needs, HIS readiness, HIS constraints, and HIS technology integration elements and standards tied to the EA development architectural layers of The Open Group Architecture Framework (TOGAF) as an EA development methodology. Eventually, the themes were conceptualised into a framework reviewed by two experts. The essence of the study was to provide a framework within which interoperable EA of HISs should be developed.

Keywords: enterprise architecture, eHealth, health information systems, interoperability

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Authors: Mohammad Sheikhalishahi, Vahid Ebrahimipour, Amir Hossein Radman-Kian

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This paper proposes an Ontology framework for knowledge modeling and representation of the equipment integrity process in a typical oil and gas production plant. Our aim is to construct a knowledge modeling that facilitates translation, interpretation, and conversion of human-readable integrity interpretation into computer-readable representation. The framework provides a function structure related to fault propagation using ISO 14224 and ISO 15926 OWL-Lite/ Resource Description Framework (RDF) to obtain a generic system-level model of asset integrity that can be utilized in the integrity engineering process during the equipment life cycle. It employs standard terminology developed by ISO 15926 and ISO 14224 to map textual descriptions of equipment failure and then convert it to a causality-driven logic by semantic interpretation and computer-based representation using Lite/RDF. The framework applied for an offshore gas riser. The result shows that the approach can cross-link the failure-related integrity words and domain-specific logic to obtain a representation structure of equipment integrity with causality inference based on semantic extraction of inspection report context.

Keywords: asset integrity modeling, interoperability, OWL, RDF/XML

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Authors: Lokes Parvatha Kumaran S., Sakthi Jay Mahenthar C., Sathyaprakash P., Jayakumar V., Shobanadevi A.

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With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. But even the most powerful classical supercomputers find it difficult to execute these tasks. With the recent development of quantum computing, researchers and tech-giants strive for new quantum circuits for machine learning tasks, as present works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. But it is strenuous to simulate classical deep learning models on existing quantum computing platforms due to the inflexibility of deep quantum circuits. As a consequence, it is essential to design viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Reinforcement Learning by remodeling the experience replay and target network into a representation of VQC. In addition, to reduce the number of model parameters, quantum information encoding schemes are used to achieve better results than the classical neural networks. VQCs are employed to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and the target network.

Keywords: quantum computing, quantum machine learning, variational quantum circuit, deep reinforcement learning, quantum information encoding scheme

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Authors: Sajjad Akbar, Rabia Bashir

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With the growth of the number of users, the Internet usage has evolved. Due to its key design principle, there is an incredible expansion in its size. This tremendous growth of the Internet has brought new applications (mobile video and cloud computing) as well as new user’s requirements i.e. content distribution environment, mobility, ubiquity, security and trust etc. The users are more interested in contents rather than their communicating peer nodes. The current Internet architecture is a host-centric networking approach, which is not suitable for the specific type of applications. With the growing use of multiple interactive applications, the host centric approach is considered to be less efficient as it depends on the physical location, for this, Information Centric Networking (ICN) is considered as the potential future Internet architecture. It is an approach that introduces uniquely named data as a core Internet principle. It uses the receiver oriented approach rather than sender oriented. It introduces the naming base information system at the network layer. Although ICN is considered as future Internet architecture but there are lot of criticism on it which mainly concerns that how ICN will manage the most relevant content. For this Web Content Mining(WCM) approaches can help in appropriate data management of ICN. To address this issue, this paper contributes by (i) discussing multiple ICN approaches (ii) analyzing different Web Content Mining approaches (iii) creating a new Internet architecture by merging ICN and WCM to solve the data management issues of ICN. From ICN, Content-Centric Networking (CCN) is selected for the new architecture, whereas, Agent-based approach from Web Content Mining is selected to find most appropriate data.

Keywords: agent based web content mining, content centric networking, information centric networking

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Authors: Claude Tadonki

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We address the issues related to the computation of the covariance matrix. This matrix is likely to be ill conditioned following its canonical expression, thus consequently raises serious numerical issues. The underlying linear system, which therefore should be solved by means of iterative approaches, becomes computationally challenging. A huge number of iterations is expected in order to reach an acceptable level of convergence, necessary to meet the required accuracy of the computation. In addition, this linear system needs to be solved at each iteration following the general form of the covariance matrix. Putting all together, its comes that we need to compute as fast as possible the associated matrix-vector product. This is our purpose in the work, where we consider and discuss skillful formulations of the problem, then propose a parallel implementation of the matrix-vector product involved. Numerical and performance oriented discussions are provided based on experimental evaluations.

Keywords: covariance-matrix, multicore, numerical computing, parallel computing

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Authors: H. Parveen Begam, M. A. Maluk Mohamed

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Mobile Grid Computing is an environment that allows sharing and coordinated use of diverse resources in dynamic, heterogeneous and distributed environment using different types of electronic portable devices. In a grid environment the security issues are like authentication, authorization, message protection and delegation handled by GSI (Grid Security Infrastructure). Proving better security between mobile devices and grid infrastructure is a major issue, because of the open nature of wireless networks, heterogeneous and distributed environments. In a mobile grid environment, the individual computing devices may be resource-limited in isolation, as an aggregated sum, they have the potential to play a vital role within the mobile grid environment. Some adaptive methodology or solution is needed to solve the issues like authentication of a base station, security of information flowing between a mobile user and a base station, prevention of attacks within a base station, hand-over of authentication information, communication cost of establishing a session key between mobile user and base station, computing complexity of achieving authenticity and security. The sharing of resources of the devices can be achieved only through the trusted relationships between the mobile hosts (MHs). Before accessing the grid service, the mobile devices should be proven authentic. This paper proposes the dynamic certificate based mutual authentication protocol between two mobile hosts in a mobile grid environment. The certificate generation process is done by CA (Certificate Authority) for all the authenticated MHs. Security (because of validity period of the certificate) and dynamicity (transmission time) can be achieved through the secure service certificates. Authentication protocol is built on communication services to provide cryptographically secured mechanisms for verifying the identity of users and resources.

Keywords: mobile grid computing, certificate authority (CA), SSL/TLS protocol, secured service certificates

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Authors: Shayan Mohajer Hamidi

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Federated learning has emerged as a promising approach for distributed model training while preserving data privacy. However, the challenges of communication overhead, limited network resources, and slow convergence hinder its widespread adoption. On the other hand, knowledge distillation has shown great potential in compressing large models into smaller ones without significant loss in performance. In this paper, we propose an innovative framework that combines federated learning and knowledge distillation to address these challenges and enhance the efficiency of distributed learning. Our approach, called Federated Knowledge Distillation (FKD), enables multiple clients in a federated learning setting to collaboratively distill knowledge from a teacher model. By leveraging the collaborative nature of federated learning, FKD aims to improve model compression while maintaining privacy. The proposed framework utilizes a coded teacher model that acts as a reference for distilling knowledge to the client models. To demonstrate the effectiveness of FKD, we conduct extensive experiments on various datasets and models. We compare FKD with baseline federated learning methods and standalone knowledge distillation techniques. The results show that FKD achieves superior model compression, faster convergence, and improved performance compared to traditional federated learning approaches. Furthermore, FKD effectively preserves privacy by ensuring that sensitive data remains on the client devices and only distilled knowledge is shared during the training process. In our experiments, we explore different knowledge transfer methods within the FKD framework, including Fine-Tuning (FT), FitNet, Correlation Congruence (CC), Similarity-Preserving (SP), and Relational Knowledge Distillation (RKD). We analyze the impact of these methods on model compression and convergence speed, shedding light on the trade-offs between size reduction and performance. Moreover, we address the challenges of communication efficiency and network resource utilization in federated learning by leveraging the knowledge distillation process. FKD reduces the amount of data transmitted across the network, minimizing communication overhead and improving resource utilization. This makes FKD particularly suitable for resource-constrained environments such as edge computing and IoT devices. The proposed FKD framework opens up new avenues for collaborative and privacy-preserving distributed learning. By combining the strengths of federated learning and knowledge distillation, it offers an efficient solution for model compression and convergence speed enhancement. Future research can explore further extensions and optimizations of FKD, as well as its applications in domains such as healthcare, finance, and smart cities, where privacy and distributed learning are of paramount importance.

Keywords: federated learning, knowledge distillation, knowledge transfer, deep learning

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Authors: Adegoriola Mayowa I., Lai Joseph H. K., Yung Esther H. K., Chan Edwin H. K.

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Heritage buildings (HBs) as structures with historical and architectural relevance that form an integral part of contemporary society. These buildings deserve to be protected for as long as possible to retain their significance. Therefore, the need to prioritize HB maintenance management is pertinent. However, the decision-making process of HBMM can be relatively daunting. The decision-making challenge may be attributed to the multiple 'stakeholders' expectation and requirement which needs to be met. To this end, professionals in the built environment have identified the need to apply the strategic concept of facilities management (FM) in decision making. Furthermore, the different maintenance dimensions have been applied to maintenance management of residential, commercial, and health facilities. Unfortunately, these different maintenance approaches, such as FM, sustainable FM, urban FM, green FM, and strategic FM, are yet to be fully explored in the decision-making process of HBMM. To bridge this gap, this study focuses on developing a framework for strategic decision-making HBMM, which helps achieve HBMM sustainability. At the study's inception, relevant works of literature in the domains of HBMM and FM were conducted. This review helped in the identification of contemporary maintenance practices and their applicability to HBMM. Afterward, a conceptual framework to aid decision-making in HBMM was developed. This framework integrated the concept of FM scope (people, place, process, and technology) while ensuring that decisions plans were made at strategic, tactical, and operational levels. Also, the different characteristics of HBs and stakeholders' requirements were considered in the framework. The conceptual framework presents a holistic guide for professionals in HBMM to ensure that decision processes and outcomes are practical and efficient. It also contributes to the existing body of knowledge on the integration of FM in HBMM. Furthermore, it will serve as a basis for future studies by applying the conceptualized framework in actual cases.

Keywords: decision-making, facility management, strategy, sustainability, heritage building, maintenance

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Authors: Alma Mendoza-Ponce, Rogelio Corona-Núñez, Florian Kraxner

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Mexico is a megadiverse country and it has nearly halved its natural vegetation in the last century due to agricultural and livestock expansion. Impacts of land use cover change and climate change are unevenly distributed and spatial prioritization to minimize the affectations on biodiversity is crucial. Global and national efforts for prioritizing biodiversity conservation show that ~33% to 45% of Mexico should be protected. The width of these targets makes difficult to lead resources. We use a framework based on vulnerability and irreplaceability to prioritize conservation efforts in Mexico. Vulnerability considered exposure, sensitivity and adaptive capacity under two scenarios (business as usual, BAU based, on the SSP2 and RCP 4.5 and a Green scenario, based on the SSP1 and the RCP 2.6). Exposure to land use is the magnitude of change from natural vegetation to anthropogenic covers while exposure to climate change is the difference between current and future values for both scenarios. Sensitivity was considered as the number of endemic species of terrestrial vertebrates which are critically endangered and endangered. Adaptive capacity is used as the ration between the percentage of converted area (natural to anthropogenic) and the percentage of protected area at municipality level. The results suggest that by 2050, between 11.6 and 13.9% of Mexico show vulnerability ≥ 50%, and by 2070, between 12.0 and 14.8%, in the Green and BAU scenario, respectively. From an ecosystem perspective cloud forests, followed by tropical dry forests, natural grasslands and temperate forests will be the most vulnerable (≥ 50%). Amphibians are the most threatened vertebrates; 62% of the endemic amphibians are critically endangered or endangered while 39%, 12% and 9% of the mammals, birds, and reptiles, respectively. However, the distribution of these amphibians counts for only 3.3% of the country, while mammals, birds, and reptiles in these categories represent 10%, 16% and 29% of Mexico. There are 5 municipalities out of the 2,457 that Mexico has that represent 31% of the most vulnerable areas (70%).These municipalities account for 0.05% of Mexico. This multiscale approach can be used to address resources to conservation targets as ecosystems, municipalities or species considering land use cover change, climate change and biodiversity uniqueness.

Keywords: biodiversity, climate change, land use change, Mexico, vulnerability

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Authors: Jia-Jang Wu

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The objective of this research is to develop a general technique so that one may predict the dynamic behaviour of a three-dimensional scale crane model subjected to time-dependent moving point forces by means of conventional finite element computer packages. To this end, the whole scale crane model is divided into two parts: the stationary framework and the moving substructure. In such a case, the dynamic responses of a scale crane model can be predicted from the forced vibration responses of the stationary framework due to actions of the four time-dependent moving point forces induced by the moving substructure. Since the magnitudes and positions of the moving point forces are dependent on the relative positions between the trolley, moving substructure and the stationary framework, it can be found from the numerical results that the time histories for the moving speeds of the moving substructure and the trolley are the key factors affecting the dynamic responses of the scale crane model.

Keywords: moving load, moving substructure, dynamic responses, forced vibration responses

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Authors: Elizabeth Radian

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In a challenging era of neoliberalism and managerialism in social services, the status of Canadian social work education at the Bachelor of Social Work level (BSW) was examined to determine how prepared students were to practice in a time of resource cutbacks and insecurity. Curricula in BSW programs was the focus as this generalist degree results in the greatest number of social work graduates in Canada, most of whom work at the front lines in service delivery. The study reviewed the practice frameworks that students in BSW programs were exposed to. Traditionally, schools of social work have embraced two major practice frameworks. The person in environment framework is a well-established practice framework taught in most schools. The framework offers some focus on smaller scale social change, tweaking existing arrangements and is more accepting of the status quo. An alternate practice framework taught in fewer schools has been described as a structural, progressive or anti oppressive framework. This latter framework challenges the status quo, is focused on social justice and social transformation, often incorporating social action strategies to ensure marginalized voices are heard. Using a content analysis methodology of keywords and phrases to delineate framework orientation, practice frameworks articulated in the curricula were determined by reviewing the mission/mandate of schools offering a BSW degree, their core course outlines and core course textbooks. Social action, as one strategy for initiating social change and transformation was considered. Initial research for 28 schools was completed in 2000, with follow up replications of the initial study in 2005 and 2014. These earlier studies displayed that the dominant practice framework taught in BSW programs was the person in environment framework. A lesser number of schools were categorized as primarily offering a structural, progressive or anti oppressive framework. The findings from the current study of 39 Canadian schools of social work are considered to determine how prominent structural, progressive and anti oppressive frameworks exist in current BSW curricula. This study can assist in contemplating the question – are we educating future practitioners for acceptance or action.

Keywords: social work education and pedagogy, social change, social justice, social services

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Authors: Mohammed Aladalah

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The emergence of digital citizens is no longer mere speculation; therefore, governments’ use of Web 2.0 tools (hereafter Gov 2.0) should be a part of all current and future e-government plans. The potential of Gov 2.0 to facilitate greater communication, participation, and collaboration with citizens has been highlighted and discussed extensively in recent literature. However, the current levels of citizens’ participation in Gov 2.0 have not lived up to the hype. Therefore, governments need to rethink the way in which they implement Gov 2.0, and take advantage of the digitally-engaged population. We propose a two-dimensional framework to tackle this issue: first, on the supply side, governments tend to use Gov 2.0 mainly for the dissemination of information and for self-promotion without the desire to encourage any interaction with citizens; this is due to many reasons, including the lack of time and the possibility of loss of control. The second dimension of the framework is the demand side; citizens are unwilling to participate in Gov 2.0 activities because they do not perceive its value or trust the government. We attempt to consider the elements of both supply and demand in order to provide a comprehensive solution whereby the potential of Gov 2.0 can be fully utilized. Our framework is based on the theoretical foundation of service science and value co-creation theory. This paper makes two significant contributions: (a) it provides an initial framework intended to increase citizens’ participation in Gov 2.0; and (b) it enhances the understanding of the government’s Gov 2.0 applications, particularly in terms of factors that ensure their attractiveness for citizens. This work is the first step in a comprehensive research undertaking, the purpose of which is to study public’s engagement with the Gov 2.0 concept. It contributes to providing a better understanding of e-government and its future.

Keywords: e-government, Gov 2.0, citizens participation, digital citizen

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Authors: Xu Jie

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As a key network security method, cryptographic services must fully cope with problems such as the wide variety of cryptographic algorithms, high concurrency requirements, random job crossovers, and instantaneous surges in workloads. Its complexity and dynamics also make it difficult for traditional static security policies to cope with the ever-changing situation. Cyber Threats and Environment. Traditional resource scheduling algorithms are inadequate when facing complex decision-making problems in dynamic environments. A network cryptographic resource allocation algorithm based on reinforcement learning is proposed, aiming to optimize task energy consumption, migration cost, and fitness of differentiated services (including user, data, and task security) by modeling the multi-job collaborative cryptographic service scheduling problem as a multi-objective optimized job flow scheduling problem and using a multi-agent reinforcement learning method, efficient scheduling and optimal configuration of cryptographic service resources are achieved. By introducing reinforcement learning, resource allocation strategies can be adjusted in real-time in a dynamic environment, improving resource utilization and achieving load balancing. Experimental results show that this algorithm has significant advantages in path planning length, system delay and network load balancing and effectively solves the problem of complex resource scheduling in cryptographic services.

Keywords: cloud computing, cryptography on-demand service, reinforcement learning, workflow scheduling

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Authors: Mariam Shah

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This paper will examine the potential influence and role of unconscious cognition on judicial decision making. The theoretical underpinnings of this paper rest on phenomenological theory grounded predominantly in Schutzian phenomenology. Aspects of Husserlian and Gadamerian phenomenology will be included within the phenomenological framework put forward in this paper, in an attempt to provide a more complete and thorough account of how unconscious cognition can influence judicial decision making. This paper has far reaching implications, as the framework provides a foundation for unconscious cognitive factors which can work to influence decision making more generally.

Keywords: decision making, Gadamer, Gadamerian, Husserl, Husserlian, judicial decision making, phenomenology, Schutz, Schutzian

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Authors: Vesna Karapetkovska Hristova, M. Ayaz Ahmad, Julijana Tomovska, Biljana Bogdanova Popov, Blagojce Najdovski

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The average total concentrations of heavy metals; (cadmium [Cd], copper [Cu], nickel [Ni], lead [Pb], and zinc [Zn]) were analyzed in soil and vegetables samples collected from the different region of Macedonia during the years 2010-2012. Basic soil properties such as pH, organic matter and clay content were also included in the study. The average concentrations of Cd, Cu, Ni, Pb, Zn in the A horizon (0-30 cm) of agricultural soils were as follows, respectively: 0.25, 5.3, 6.9, 15.2, 26.3 mg kg-1 of soil. We have found that neural networking model can be considered as a tool for prediction and spatial analysis of the processes controlling the metal transfer within the soil-and vegetables. The predictive ability of such models is well over 80% as compared to 20% for typical regression models. A radial basic function network reflects good predicting accuracy and correlation coefficients between soil properties and metal content in vegetables much better than the back-propagation method. Neural Networking / soft computing can support the decision-making processes at different levels, including agro ecology, to improve crop management based on monitoring data and risk assessment of metal transfer from soils to vegetables.

Keywords: soft computing approach, total concentrations, heavy metals, agricultural soils

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Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

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Authors: Jacob Wahl, Jane Zhang

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This paper describes the development and characterization of a prototype IoT network for use with agricultural imaging and monitoring applications. The sensor and gateway nodes are designed using the ESP32 SoC with integrated Bluetooth Low Energy 4.2 and Wi-Fi. A development board, the Arducam IoTai ESP32, is used for prototyping, testing, and power measurements. Google’s Firebase is used as the cloud storage site for image data collected by the sensor. The sensor node captures images using the OV2640 2MP camera module and transmits the image data to the gateway via Bluetooth Low Energy. The gateway then uploads the collected images to Firebase via a known nearby Wi-Fi network connection. This image data can then be processed and analyzed by computer vision and machine learning pipelines to assess crop growth or other needs. The sensor node achieves a wireless transmission data throughput of 220kbps while consuming 150mA of current; the sensor sleeps at 162µA. The sensor node device lifetime is estimated to be 682 days on a 6600mAh LiPo battery while acquiring five images per day based on the development board power measurements. This network can be utilized by any application that requires high data rates, low power consumption, short-range communication, and large amounts of data to be transmitted at low-frequency intervals.

Keywords: Bluetooth low energy, ESP32, firebase cloud, IoT, smart farming

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Authors: Mohammed Hmimou, Khalid Amediaz, Imane Sebari, Nabil Bounajma

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Annual crop production is one of the most important macroeconomic indicators for the majority of countries around the world. This information is valuable, especially for exporting countries which need a yield estimation before harvest in order to correctly plan the supply chain. When it comes to estimating agricultural yield, especially for arboriculture, conventional methods are mostly applied. In the case of the citrus industry, the sale before harvest is largely practiced, which requires an estimation of the production when the fruit is on the tree. However, conventional method based on the sampling surveys of some trees within the field is always used to perform yield estimation, and the success of this process mainly depends on the expertise of the ‘estimator agent’. The present study aims to propose a methodology based on the combination of unmanned aerial vehicle (UAV) images and terrestrial laser scanner (TLS) point cloud to estimate citrus production. During data acquisition, a fixed wing and rotatory drones, as well as a terrestrial laser scanner, were tested. After that, a pre-processing step was performed in order to generate point cloud and digital surface model. At the processing stage, a machine vision workflow was implemented to extract points corresponding to fruits from the whole tree point cloud, cluster them into fruits, and model them geometrically in a 3D space. By linking the resulting geometric properties to the fruit weight, the yield can be estimated, and the statistical distribution of fruits size can be generated. This later property, which is information required by importing countries of citrus, cannot be estimated before harvest using the conventional method. Since terrestrial laser scanner is static, data gathering using this technology can be performed over only some trees. So, integration of drone data was thought in order to estimate the yield over a whole orchard. To achieve that, features derived from drone digital surface model were linked to yield estimation by laser scanner of some trees to build a regression model that predicts the yield of a tree given its features. Several missions were carried out to collect drone and laser scanner data within citrus orchards of different varieties by testing several data acquisition parameters (fly height, images overlap, fly mission plan). The accuracy of the obtained results by the proposed methodology in comparison to the yield estimation results by the conventional method varies from 65% to 94% depending mainly on the phenological stage of the studied citrus variety during the data acquisition mission. The proposed approach demonstrates its strong potential for early estimation of citrus production and the possibility of its extension to other fruit trees.

Keywords: citrus, digital surface model, point cloud, terrestrial laser scanner, UAV, yield estimation, 3D modeling

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Authors: U. Yerlikaya, R. T. Balkan

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In this research, a method is developed to obtain high-dimensional configuration space for path planning problems. In typical cases, the path planning problems are solved directly in the 3-dimensional (D) workspace. However, this method is inefficient in handling the robots with various geometrical and mechanical restrictions. To overcome these difficulties, path planning may be formalized and solved in a new space which is called configuration space. The number of dimensions of the configuration space comes from the degree of freedoms of the system of interest. The method can be applied in two ways. In the first way, the point clouds of all the bodies of the system and interaction of them are used. The second way is performed via using the clearance function of simulation software where the minimum distances between surfaces of bodies are simultaneously measured. A double-turret system is held in the scope of this study. The 4-D configuration space of a double-turret system is obtained in these two ways. As a result, the difference between these two methods is around 1%, depending on the density of the point cloud. The disparity between the two forms steadily decreases as the point cloud density increases. At the end of the study, in order to verify 4-D configuration space obtained, 4-D path planning problem was realized as 2-D + 2-D and a sample path planning is carried out with using A* algorithm. Then, the accuracy of the configuration space is proved using the obtained paths on the simulation model of the double-turret system.

Keywords: A* algorithm, autonomous turrets, high-dimensional C-space, manifold C-space, point clouds

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Authors: Inaki Maulida Hakim, T. Yuri M. Zagloel, Astari Wulandari

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Six sigma is a framework that is used to identify inefficiency so that the cause of inefficiency will be known and right improvement to overcome cause of inefficiency can be conducted. This paper presents result of implementing six sigma to improve piston assembly line in Manufacturing Laboratory, Universitas Indonesia. Six sigma framework will be used to analyze the significant factor of inefficiency that needs to be improved which causes bottleneck in assembly line. After analysis based on six sigma framework conducted, line balancing method was chosen for improvement to overcome causative factor of inefficiency which is differences time between workstation that causes bottleneck in assembly line. Then after line balancing conducted in piston assembly line, the result is increase in efficiency. Efficiency is shown in the decreasing of Defects per Million Opportunities (DPMO) from 900,000 to 700,000, the increasing of level of labor productivity from 0.0041 to 0.00742, the decreasing of idle time from 121.3 seconds to 12.1 seconds, and the increasing of output, which is from 1 piston in 5 minutes become 3 pistons in 5 minutes.

Keywords: assembly line, line balancing, productivity, six sigma

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Authors: Tareq Aljaber

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The number of mobile health apps is growing at a fast frequency as it's nearly doubled in a year between 2015 and 2016. Though, there is a lack of an effective evaluation framework to verify the usability and reliability of mobile phone health education applications which would help saving time and effort for the numerous user groups. This abstract describing a framework for evaluating mobile applications in specifically mobile health education applications, along with a guidance select tool to assist different users to select the most suitable mobile health education apps. The effective framework outcome is intended to meet the requirements and needs of the different stakeholder groups additionally to enhancing the development of mobile health education applications with software engineering approaches, by producing new and more effective techniques to evaluate such software. This abstract highlights the significance and consequences of mobile health education apps, before focusing the light on the required to create an effective evaluation framework for these apps. An explanation of the effective evaluation framework is going to be delivered in the abstract, beside with some specific evaluation metrics: an efficient hybrid of selected heuristic evaluation (HE) and usability evaluation (UE) metrics to enable the determination of the usefulness and usability of health education mobile apps. Moreover, an explanation of the qualitative and quantitative outcomes for the effective evaluation framework was accomplished using Epocrates mobile phone app in addition to some other mobile phone apps. This proposed framework-An Evaluation Framework for Mobile Health Education Apps-consists of a hybrid of 5 metrics designated from a larger set in usability evaluation and heuristic evaluation, illuminated grounded on 15 unstructured interviews from software developers (SD), health professionals (HP) and patients (P). These five metrics corresponding to explicit facets of usability recognised through a requirements analysis of typical stakeholders of mobile health apps. These five hybrid selected metrics were scattered across 24 specific questionnaire questions, which are available on request from first author. This questionnaire has been sent to 81 participants distributed in three sets of stakeholders from software developers (SD), health professionals (HP) and patients/general users (P/GU) on the purpose of ranking three sets of mobile health education applications. Finally, the outcomes from the questionnaire data helped us to approach our aims which are finding the profile for different stakeholders, finding the profile for different mobile health educations application packages, ranking different mobile health education application and guide us to build the select guidance too which is apart from the Evaluation Framework for Mobile Health Education Apps.

Keywords: evaluation framework, heuristic evaluation, usability evaluation, metrics

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Authors: Moon-Soo Kim

Abstract:

In order to cope with the increasing demand for multidisciplinary learning between academy and industry, a collaborative teaching and learning model and related operational tools enabling applications to engineering education are essential. This study proposes a web-based collaborative framework for interactive teaching and learning between academy and industry as an initial step for the development of a web- and mobile-based integrated system for both engineering students and industrial practitioners. The proposed web-based collaborative teaching and learning framework defines several entities such as learner, solver and supporter or sponsor for industrial problems, and also has a systematic architecture to build information system including diverse functions enabling effective interaction among the defined entities regardless of time and places. Furthermore, the framework, which includes knowledge and information self-reinforcing mechanism, focuses on the previous problem-solving records as well as subsequent learners’ creative reusing in solving process of new problems.

Keywords: collaborative teaching and learning model, academy and industry, web-based collaborative framework, self-reinforcing mechanism

Procedia PDF Downloads 326

Authors: Arezoo Sadrinezhad, Kallol Sett, S. I. Hariharan

Abstract:

In this paper, a probabilistic framework based on Fokker-Planck-Kolmogorov (FPK) approach has been applied to simulate triaxial cyclic constitutive behavior of uncertain soils. The framework builds upon previous work of the writers, and it has been extended for cyclic probabilistic simulation of triaxial undrained behavior of soils. von Mises elastic-perfectly plastic material model is considered. It is shown that by using probabilistic framework, some of the most important aspects of soil behavior under cyclic loading can be captured even with a simple elastic-perfectly plastic constitutive model.

Keywords: elasto-plasticity, uncertainty, soils, fokker-planck equation, fourier spectral method, finite difference method

Procedia PDF Downloads 379

Authors: Abongile Zweni

Abstract:

As a post-apartheid strategy to redress the social imbalances of the past, local governments are tasked with the role of delivering crucial services to their constituents. Apart from political instability, evidence shows that managers in South African municipalities lack effective financial management skills and competencies. This resulted in a failure to fulfill its administrative obligations, particularly municipal financial management. Most municipalities have, however, failed in this role, which has led them to be placed under administration by the provincial government in terms of Section 139 of the constitution of the Republic of South Africa. Thus, this study proposed a leadership conceptual framework for effectively managing ever-eroding municipal finances in South Africa. The study adopted a desktop research approach to explore the key components of leadership and municipal financial management toward the development of the conceptual framework. The study fostered a better understanding of the need for transformation in relation to the current financial management practices and sustainability of a municipality. Moreover, the conceptual framework applies not only to municipalities but also to other government departments and public authorities in the country for financial management.

Keywords: leadership, municipal finance, financial performance, management skills, municipality

Procedia PDF Downloads 311

Authors: Sylvie Michel, François Cocula

Abstract:

Digital agriculture is an approach that exploits information and communication technologies. These encompass data acquisition tools like mobile applications, satellites, sensors, connected devices, and smartphones. Additionally, it involves transfer and storage technologies such as 3G/4G coverage, low-bandwidth terrestrial or satellite networks, and cloud-based systems. Furthermore, embedded or remote processing technologies, including drones and robots for process automation, along with high-speed communication networks accessible through supercomputers, are integral components of this approach. While farm-level adoption studies regarding digital agricultural technologies have emerged in recent years, they remain relatively limited in comparison to other agricultural practices. To bridge this gap, this study delves into understanding farmers' intention to adopt digital tools, employing the technology, organization, environment framework. A qualitative research design encompassed semi-structured interviews, totaling fifteen in number, conducted with key stakeholders both prior to and following the 2020-2021 COVID-19 lockdowns in France. Subsequently, the interview transcripts underwent thorough thematic content analysis, and the data and verbatim were triangulated for validation. A coding process aimed to systematically organize the data, ensuring an orderly and structured classification. Our research extends its contribution by delineating sub-dimensions within each primary dimension. A total of nine sub-dimensions were identified, categorized as follows: perceived usefulness for communication, perceived usefulness for productivity, and perceived ease of use constitute the first dimension; technological resources, financial resources, and human capabilities constitute the second dimension, while market pressure, institutional pressure, and the COVID-19 situation constitute the third dimension. Furthermore, this analysis enriches the TOE framework by incorporating entrepreneurial orientation as a moderating variable. Managerial orientation emerges as a pivotal factor influencing adoption intention, with producers acknowledging the significance of utilizing digital sales support tools to combat "greenwashing" and elevate their overall brand image. Specifically, it illustrates that producers recognize the potential of digital tools in time-saving and streamlining sales processes, leading to heightened productivity. Moreover, it highlights that the intent to adopt digital sales support tools is influenced by a market mimicry effect. Additionally, it demonstrates a negative association between the intent to adopt these tools and the pressure exerted by institutional partners. Finally, this research establishes a positive link between the intent to adopt digital sales support tools and economic fluctuations, notably during the COVID-19 pandemic. The adoption of sales support tools in agriculture is a multifaceted challenge encompassing three dimensions and nine sub-dimensions. The research delves into the adoption of digital farming technologies at the farm level through the TOE framework. This analysis provides significant insights beneficial for policymakers, stakeholders, and farmers. These insights are instrumental in making informed decisions to facilitate a successful digital transition in agriculture, effectively addressing sector-specific challenges.

Keywords: adoption, digital agriculture, e-commerce, TOE framework

Procedia PDF Downloads 60

Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

Abstract:

Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

Procedia PDF Downloads 251

Authors: Hai Zeng, Anqing Liu, Shuguang Dan, Ying Zhang, Yan Li, Zihang Zeng

Abstract:

Children are strong; the country strong, the development of children Basketball is a strategic advantage. Common forms of basketball equipment has been difficult to meet the needs of young children teaching the game of basketball, basketball development for 3-6 years old children in the form of appropriate teaching aids is a breakthrough basketball game teaching children bottlenecks, improve teaching critical path pleasure, but also the development of early childhood basketball a necessary requirement. In this study, literature, questionnaires, focus group interviews, comparative analysis, for domestic and foreign use of 12 kinds of basketball teaching aids (cloud computing MINI basketball, adjustable basketball MINI, MINI basketball court, shooting assist paw print ball, dribble goggles, dribbling machine, machine cartoon shooting, rebounding machine, against the mat, elastic belt, ladder, fitness ball), from fun and improve early childhood shooting technique, dribbling technology, as well as offensive and defensive rebounding against technology conduct research on conversion technology. The results show that by using appropriate forms of teaching children basketball aids, can effectively improve children's fun basketball game, targeted to improve a technology, different types of aids from different perspectives enrich the connotation of children basketball game. Recommended for children of color psychology, cartoon and environmentally friendly material production aids, and increase research efforts basketball aids children, encourage children to sports teachers aids applications.

Keywords: appropriate forms of children basketball, auxiliary equipment, appli, MINI basketball, 3-6 years old children, teaching

Procedia PDF Downloads 385

Authors: C. Park, H. Lee, Y-J. Lee

Abstract:

Aim: This study presents Interdisciplinary Collaborative Innovation Mechanism as a medium to enable the effective generation of innovations for sustainability challenges facing humanities. Background: Interdisciplinary approach of fusing disparate knowledge and perspectives from diverse expertise and subject areas is one of the key requirements to address the intricate nature of sustainability issues. There is a lack of rigorous empirical study of the systematic structure of interdisciplinary collaborative innovation for sustainability to date. Method: To address this research gap, the action research approach is adopted to develop the Interdisciplinary Collaborative Innovation Mechanism (ICIM) framework based on an empirical study of a total of 28 open innovation competitions in the format of MAKEathons between 2016 to 2023. First, the conceptual framework was formulated based on the literature findings, and the framework was subsequently tested and iterated. Outcomes: The findings provide the ICIM framework composed of five elements: Discipline Diversity Quadruple; Systematic Structure; Inspirational Stimuli; Supportive Collaboration Environment; and Hardware and Intellectual Support. The framework offers a discussion of the key elements when attempting to facilitate interdisciplinary collaboration for sustainability innovation. Contributions: This study contributes to two burgeoning areas of sustainable development and open innovation studies by articulating the concrete structure to bridge the gap. In practice, the framework helps facilitate effective innovation processes and positive social and environmental impact created for real-world sustainability challenges.

Keywords: action research, interdisciplinary collaboration, open innovation, problem-solving, sustainable development, sustainability challenges

Procedia PDF Downloads 247