Framework Efficiency Research Articles

A dynamic maintenance planning methodology for HVAC systems based on Fuzzy-TOPSIS and failure mode and Effect Analysis

Maintenance is critical to the efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings. The conventional maintenance methods are planned for ideal situations and cannot easily be adapted to prevailing management/operational conditions, resulting in increased use of resources and energy. This study developed a dynamic maintenance planning framework for HVAC systems employing Fuzzy-TOPSIS and FMEA and aimed at reducing and stabilising maintenance costs while enhancing system reliability. Fuzzy-TOPSIS technique was used to establish maintenance priorities based on a carefully identified set of criteria, including the risk priority number (RPN), which is adaptable to changing conditions and ensures continuous monitoring. This is a novel application to HVAC systems. A real-world implementation in an HVAC company involving data collection, fuzzification of expert evaluations, priority ranking of components, and development of maintenance ranking validated the framework's efficiency. The running conditions of the HVAC system under the new approach, expressed by the air quality and air leakages (by extension energy saving), showed a significant improvement at the various test points in the 600-h running. The results demonstrated significant cost savings, enhanced system reliability, improved energy efficiency, and better indoor air quality. This dynamic maintenance planning framework offers a concise and adaptable solution for optimising HVAC maintenance operations.

Enhancing human activity recognition for the elderly and individuals with disabilities through optimized Internet-of-Things and artificial intelligence integration with advanced neural networks.

Elderly and individuals with disabilities can greatly benefit from human activity recognition (HAR) systems, which have recently advanced significantly due to the integration of the Internet of Things (IoT) and artificial intelligence (AI). The blending of IoT and AI methodologies into HAR systems has the potential to enable these populations to lead more autonomous and comfortable lives. HAR systems are equipped with various sensors, including motion capture sensors, microcontrollers, and transceivers, which supply data to assorted AI and machine learning (ML) algorithms for subsequent analyses. Despite the substantial advantages of this integration, current frameworks encounter significant challenges related to computational overhead, which arises from the complexity of AI and ML algorithms. This article introduces a novel ensemble of gated recurrent networks (GRN) and deep extreme feedforward neural networks (DEFNN), with hyperparameters optimized through the artificial water drop optimization (AWDO) algorithm. This framework leverages GRN for effective feature extraction, subsequently utilized by DEFNN for accurately classifying HAR data. Additionally, AWDO is employed within DEFNN to adjust hyperparameters, thereby mitigating computational overhead and enhancing detection efficiency. Extensive experiments were conducted to verify the proposed methodology using real-time datasets gathered from IoT testbeds, which employ NodeMCU units interfaced with Wi-Fi transceivers. The framework's efficiency was assessed using several metrics: accuracy at 99.5%, precision at 98%, recall at 97%, specificity at 98%, and F1-score of 98.2%. These results then were benchmarked against other contemporary deep learning (DL)-based HAR systems. The experimental outcomes indicate that our model achieves near-perfect accuracy, surpassing alternative learning-based HAR systems. Moreover, our model demonstrates reduced computational demands compared to preceding algorithms, suggesting that the proposed framework may offer superior efficacy and compatibility for deployment in HAR systems designed for elderly or individuals with disabilities.

Ethereum blockchain for electronic health records: securing and streamlining patient management.

Electronic health records (EHRs) are increasingly replacing traditional paper-based medical records due to their speed, security, and ability to eliminate redundant data. However, challenges such as EHR interoperability and privacy concerns remain unresolved. Blockchain, a distributed ledger technology comprising connected, encrypted data blocks, presents a promising solution. This study explores how blockchain technology can revolutionize hospital EHR management. Our proposed solution securely transfers medical records between patients and doctors using the InterPlanetary File System (IPFS) and the Ethereum platform. Utilizing smart contracts automates data transfers, ensuring patient anonymity and reducing computational complexity while securely storing patient data on the network. Patient records are stored locally on the Ganache server, with the front end managed using HTML, CSS, ReactJS, and JavaScript, and the backend developed in Solidity. Blockchain technologies combined with Role- Based access control instead of attribute -based access control. The system's throughput increases linearly with the number of users and requests, enhancing the framework's efficiency and scalability. The minimum recorded latency is 14 ms.

Leveraging Deep Learning for Automated Reconstruction of Indoor Unstructured Elements in Scan-to-BIM

Abstract. Achieving automatic 3D reconstruction for indoor scenes is extremely useful in the field of scene understanding. Building information modeling (BIM) models are essential for lowering project costs, assisting in building planning and renovations, as well as improving building management efficiency. However, nearly all current available scan-to-BIM approaches employ manual or semi-automatic methods. These approaches concentrate solely on significant structured objects, neglecting other unstructured elements such as furniture. The limitation arises from challenges in modeling incomplete point clouds of obstructed objects and capturing indoor scene details. Therefore, this research introduces an innovative and effective reconstruction framework based on deep learning semantic segmentation and model-driven techniques to address these limitations. The proposed framework utilizes wall segment recognition, feature extraction, opening detection, and automatic modeling to reconstruct 3D structured models of point clouds with different room layouts in both Manhattan and non-Manhattan architectures. Moreover, it provides 3D BIM models of actual unstructured elements by detecting objects, completing point clouds, establishing bounding boxes, determining type and orientation, and automatically generating 3D BIM models with a parametric algorithm implemented into the Revit software. We evaluated this framework using publicly available and locally generated point cloud datasets with varying furniture combinations and layout complexity. The results demonstrate the proposed framework's efficiency in reconstructing structured indoor elements, exhibiting completeness and geometric accuracy, and achieving precision and recall values greater than 98%. Furthermore, the generated unstructured 3D BIM models keep essential real-scene characteristics such as geometry, spatial locations, numerical aspects, various shapes, and orientations compared to literature methods.

IoT-based external attacks aware secure healthcare framework using blockchain and SB-RNN-NVS-FU techniques.

In recent times, there has been widespread deployment of Internet of Things (IoT) applications, particularly in the healthcare sector, where computations involving user-specific data are carried out on cloud servers. However, the network nodes in IoT healthcare are vulnerable to an increased level of security threats. This paper introduces a secure Electronic Health Record (EHR) framework with a focus on IoT. Initially, the IoT sensor nodes are designated as registered patients and undergo initialization. Subsequently, a trust evaluation is conducted, and the clustering of trusted nodes is achieved through the application of Tasmanian Devil Optimization (STD-TDO) utilizing the Student's T-Distribution. Utilizing the Transposition Cipher-Squared random number generator-based-Elliptic Curve Cryptography (TCS-ECC), the clustered nodes encrypt four types of sensed patient data. The resulting encrypted data undergoes hashing and is subsequently added to the blockchain. This configuration functions as a network, actively monitored to detect any external attacks. To accomplish this, a feature reputation score is calculated for the network's features. This score is then input into the Swish Beta activated-Recurrent Neural Network (SB-RNN) model to classify potential attacks. The latest transactions on the blockchain are scrutinized using the Neutrosophic Vague Set Fuzzy (NVS-Fu) algorithm to identify any double-spending attacks on non-compromised nodes. Finally, genuine nodes are granted permission to decrypt medical records. In the experimental analysis, the performance of the proposed methods was compared to existing models. The results demonstrated that the suggested approach significantly increased the security level to 98%, reduced attack detection time to 1300 ms, and maximized accuracy to 98%. Furthermore, a comprehensive comparative analysis affirmed the reliability of the proposed model across all metrics. The proposed healthcare framework's efficiency is proved by the experimental evaluation.

Enhancing the driving experience of smart city users based on content delivery framework for intelligent transportation systems

Integration of several communication technologies that facilitate user access contributes to the rapid development of the smart city notion. Intelligent transportation systems (ITS) are introduced as part of smart city development to provide drivers with enhanced communication and information-sharing capabilities. The article introduces a novel ITS content delivery framework (CDF) that addresses communication outage issues. CDF-ITS uses End-to-end decision-making system modelling to examine factors such as communication, content distribution, and vehicle features. A suitable communication slot for vehicular users is determined by processing these characteristics based on outage time and variables. By allocating time-aware communication slots according to the classification of the propagation factor, outage problems may be reduced. End-to-end decision-making is used for classification and vehicle attribute balance, allowing immediate responses to user requests. The experimental outcomes show that the latency of 0.297 s, outage time of 0.0837, distributed messages of 276, and computing complexity of 11.87 are used to assess the proposed framework's efficiency across vehicle density and velocities.

A Secured Privacy-preserving Multifactor Approach for Autonomous Vehicles Using Blockchain Technology

Background and Aims: Smart vehicles are connected to the Internet of Things (IoT), bringing with them the potential to transform human existence in so-called "smart cities." Intelligent vehicle architecture revolves around the Vehicular-AdhocNetwork(VANET). The goal of a VANET is to make driving more pleasant. VANETs' message-sharing capabilities contribute to improved traffic management, reduced congestion, and safer driving. However, VANETs' usefulness could be diminished by the spread of fraudulent or erroneous messages Objectives and Methodology: For better road safety and less congestion, it guarantees secure and accurate communication between vehicles and between vehicles and infrastructure. The security and privacy of a VANET, however, can be compromised by threats, including denial-of-service (DoS), replay, and Sybil attacks. These problems can cause a rogue node to send out faulty data throughout the system. We introduce a biometrics-blockchain(BBC) approach to ensure the safety of information exchanged between vehicles in a VANET and to preserve archival data in a triedand-true environment. To protect the anonymity of users, the suggested framework makes use of biometric data to verify the identity of the sender. Results and Conclusion: As a result, the proposed BBC scheme creates a safe and reliable environment for vehicles in VANET, with the added benefit of identity tracing capabilities. To prove the effectiveness of the proposed framework, simulations were run in the urban mobility models OMNeT++, veins, and SUMO. Packet-delivery-rate(PDR), packet-loss-rate(PLR), and computing cost (CC) were used to assess the framework's efficiency. The outcomes highlighted the superiority of our innovative model over conventional methods, such as PDR slightly increased to 8-10%, PLR decreased to 20-25%, and CC also reduced to 15-20% compared to state-of-art models.

Indoor Geo-Indistinguishability: Adopting Differential Privacy for Indoor Location Data Protection

Due to the extensive applicability of Location-Based Services (LBSs) and the Global Navigation Satellite System (GNSS) failure in indoor environments, indoor positioning systems have been widely implemented. Location fingerprinting, in particular, collects the Received Signal Strength (RSS) from users' devices, allowing Location Service Providers (LSPs) to precisely identify their locations. Therefore, LSPs and potential attackers have implicit access to this sensitive data, violating users' privacy. This issue has been addressed in outdoor environments by introducing Geo-indistinguishability (GeoInd), an alternative representation of Differential Privacy (DP). In indoor environments, however, the user lacks their coordinates, posing a new difficulty. This paper presents a novel framework for implementing GeoInd for indoor environments. The proposed framework introduces two distance calculation and RSS generation methods based solely on RSS values. Moreover, involving other participants or trusted third parties is not necessary to protect privacy, regardless of the attackers' prior knowledge. The proposed framework is evaluated in a simulated environment and two experimental settings. The results validate the proposed framework's efficiency, effectiveness, and applicability in indoor environments under the GeoInd setting.

GRNN-Based Real-Time Fault Chain Prediction

This paper proposes a data-driven graphical framework for the real-time search of risky cascading fault chains (FCs). While identifying risky FCs is pivotal to alleviating cascading failures, the complex spatio-temporal dependencies among the components of the power system render challenges to modeling and analyzing FCs. Furthermore, the real-time search of risky FCs faces an inherent combinatorial complexity that grows exponentially with the size of the system. The proposed framework leverages the recent advances in graph recurrent neural networks to circumvent the computational complexities of the real-time search of FCs. The search process is formalized as a partially observable Markov decision process (POMDP), which is subsequently solved via a time-varying graph recurrent neural network (GRNN) that judiciously accounts for the inherent temporal and spatial structures of the data generated by the system. The key features of this structure include (i) leveraging the spatial structure of the data induced by the system topology, (ii) leveraging the temporal structure of data induced by system dynamics, and (iii) efficiently summarizing the system's history in the latent space of the GRNN. The proposed framework's efficiency is compared to the relevant literature on the IEEE 39-bus New England system and the IEEE 118-bus system.

A Hybrid Semantic Networks Construction Framework for Engineering Design

Abstract This paper proposes a novel framework for building semantic networks from a seed design statement using Recursive Object Modeling (ROM), Word2Vec language modeling, and vector semantic-based method. Semantic Scholar API was used to retrieve abstracts of scientific papers to build ROM-based Semantic Networks to address the design problem implied in the seed design statement, following Environment Analysis from Environment-Based Design (EBD) methodology. The proposed framework was applied to construct the semantic network for a project to design aircraft braking systems, which demonstrates the framework's efficiency. The presented research makes two major contributions: a ROM-based phrase extractor and a domain-specific language model, which is trained on the automatically collected literature abstracts. Using a manually created and assessed truth set containing 100 pairs of abstract-key phrases, the phrase extractor was evaluated by benchmarking it with two existing off-the-shelf key phrase extraction algorithms: TextRank and Rake. The ROM-based phrase extractor extracted most key phrases from target domains and showed higher precision, recall, and F-1 scores than other methods. Meanwhile, the trained project-specific language model was evaluated using the NASA thesaurus. We randomly sampled 457 pairs of connected domain-specific terms related to aircraft braking and landing knowledge. Our Skip-gram model was compared with Google's pre-trained word2vec model and a baseline word2vec model. The results demonstrated that our language model could detect the most pairs of concepts from the NASA thesaurus. The generated semantic network can be applied to design information retrieval, computer-aided design idea generation, cross-domain communication support system, and designer training tool.

A Secure Data Transfer Approach With an Efficient Key Management Over Cloud

The growth in the number of cloud users who transfer their health data have enhanced the importance of cloud technology's services and capabilities. However, transferring patient health data to the cloud leaves researchers with several concerns and obstacles in privacy, storage, access, key-formation, and management. The paper presents an efficient methodology for storing and accessing health information to and from the cloud. The symmetric key cryptography with the MD5 hash function is employed to enhance the framework's efficiency. The proposed method also provides secure data sharing and removes the burden of an exhaustive re-encryption computation. In the paper, two different keys are computed: one key for each legitimate user among a group and another key for the crypto-system, which is responsible to do all computations over the data. The method provides security against internal threats since only a single share of the key can be accessed. The efficiency of the model is measured by measuring the execution time for key formation, encryption, and decryption processes.

Performance Analysis of PTS PAPR Reduction Method for NOMA Waveform

Cellular systems utilize single and multicarrier waveforms for high-speed data transmission. The Fifth-Generation (5G) system proposes several techniques based on multicarrier waveforms. However, the Peak to Average Power Ratio (PAPR) is one of the significant concerns in advanced waveforms as it degrades the framework's efficiency. Non Orthogonal Multiple Access (NOMA) can provide massive connectivity, which is the crucial requirement of the Internet of Things (IoT). The 3rd generation tested NOMA applications in downlink and uplink transmission. However, NOMA uplink transmission in the power domain has performance degradation and is not considered a possible technique in 3rd generation power projects (3GPP). Presently, NOMA is not just a concept but has been introduced as a standard method in various transmission schemes. NOMA suffers from a high peak to average power ratio (PAPR), which forces the power amplifier (PA) to shift into a nonlinear region. The high value of PAPR leads the power amplifier of the multicarrier technique into a nonlinear region. As the performance of the power amplifier degrades, the overall system performance goes down. In this work, we suggest different methods to overcome the issue of PAPR in multicarrier techniques such as Selective Mapping (SLM) and Partial Transmit Sequence (PTS). The simulation results reveal that the performance of the proposed PTS is better than that of the conventional SLM and PTS.

Design, Implementation, and Evaluation of a Distance Learning Framework to Adapt to the Changing Landscape of Anatomy Instruction in Medical Education During COVID-19 Pandemic: A Proof-of-Concept Study.

This study presents the design of a DL-framework to deliver anatomy teaching that provides a microfiche of the onsite anatomy learning experience during the mandated COVID-19 lockdown. First, using nominal-group technique, we identified the DL learning theories to be employed in blueprinting the DL-framework. Effectiveness of the designed DL-framework in anatomy teaching was demonstrated using the exemplar of the Head and Neck (H&N) course during COVID-19 lockdown, in the pre-clerkship curriculum at our medical school. The dissemination of the DL-framework in the anatomy course was informed by the Analyse, Design, Develop, Implement, and Evaluate (ADDIE) model. The efficiency of the DL-framework was evaluated using the first two levels of Kirkpatrick's model. Versatility of the DL-framework was demonstrated by aligning its precepts with individual domains of key learning outcomes framework. The framework's blueprint was designed amalgamating principles of: Garrison's community inquiry, Siemens' connectivism and Harasim's online-collaborative-learning; and improved using Anderson's DL-model. Following the implementation of the DL-framework in the H&N course informed by ADDIE, the framework's efficiency was evaluated. In total, 70% students responded to the survey assessing perception toward DL (Kirkpatrick's Level: 1). Descriptive analysis of the survey results showed that the DL-framework was positively received by students and attested that students had an enriched learning experience, which promoted collaborative-learning and student-autonomy. For, Kirkpatrick's Level: 2 i.e., cognitive development, we compared the summative assessment performance in the H&N course across three cohort of students. The results show that the scores of the cohort, which experienced the course entirely through DL modality was statistically higher (P < 0.01) than both the other cohorts, indicating that shift to DL did not have an adverse effect on students' learning. Using Bourdieu's Theory of Practice, we showed that the DL-framework is an efficient pedagogical approach, pertinent for medical schools to adopt; and is versatile as it attests to the key domains of students' learning outcomes in the different learning outcomes framework. To our knowledge this is the first-study of its kind where a rationale and theory-guided approach has been availed not only to blueprint a DL framework, but also to implement it in the MBBS curriculum.

Optimal, dynamic and reliable demand-response via OpenADR-compliant multi-agent virtual nodes: Design, implementation & evaluation

Extracting and exploiting the flexibility of electric demand has been shown to reduce the needs of network upgrades and generation capacity increases. Demand Response (DR) in considered as one of the few available solutions for accessing the untapped energy potential of small and medium customers. Over the past decade, rigorous research has produced significant results in optimally dispatching DR in an attempt to maximize flexibility extraction. However, the vast majority of works assumes a “happy path” scenario in which DR requests are always successfully completed. Hence, there is a large gap in the literature that fails to account for non-deterministic factors that manifest in practical deployments, e.g., the stochasticity of end-user behavior that can drastically influence the DR's outcomes. Investing on that notion, a novel, distributed, multi-agent system (MAS) that aggregates consumers and prosumers and handles automatically OpenADR-compliant DR requests is introduced, following virtual power plant (VPP) principles. Agents of the proposed MAS are able to service DR events originating from a higher level, e.g., Aggregators or Utilities, and optimally dispatch them to their assigned customers. The proposed framework ensures 100% DR success rate, compared to conventional methods, by not only optimally exploiting aggregated flexibility through a combination of clustering and optimisation engines, but also through a dynamic, bi-directional DR matchmaking process that can mitigate observed deviations both internally (intra), as well as, externally (inter) in real-time. Via experimentation, we demonstrate the framework's efficiency in ensuring technical DR fault-tolerance along with its ability to deliver savings of up to 3 orders of magnitude to Aggregators and the customers serving the DR requests.

Aerodynamic shape optimization of civil structures: A CFD-enabled Kriging-based approach

In the case of mega-structures such as tall buildings and long-span bridges, the mitigation of the intensity of the wind excitation through aerodynamic tailoring of the external shape can be fundamental for meeting the performance goals. The search for the best performing shape through an automatic CFD-enabled optimization methodology is potentially less expensive, less time-consuming and more thorough than the common trial-and-error approach based on wind tunnel test results, therefore very attractive. This paper investigates the possibility of carrying out the multi-objective aerodynamic shape optimization of civil structures through an approach in which evolutionary algorithms are used in synergy with ordinary Kriging surrogates. A specifically developed strategy is adopted to update the Kriging models making efficient use of additional CFD runs. Shell scripting, parallelized computations and mesh morphing algorithms are exploited for enhancing the framework's efficiency and consistency. As a case study, the optimization of the shape of a tall building cross-section in terms of both the lift and the drag coefficient is considered.