Efficiency Of Method Research Articles

Building Category Graphs Representation with Spatial and Temporal Attention for Visual Navigation

Given an object of interest, visual navigation aims to reach the object’s location based on a sequence of partial observations. To this end, an agent needs to (1) acquire specific knowledge about the relations of object categories in the world during training and (2) locate the target object based on the pre-learned object category relations and its trajectory in the current unseen environment. In this article, we propose a Category Relation Graph (CRG) to learn the knowledge of object category layout relations and a Temporal-Spatial-Region attention (TSR) architecture to perceive the long-term spatial-temporal dependencies of objects, aiding navigation. We establish CRG to learn prior knowledge of object layout and deduce the positions of specific objects. Subsequently, we propose the TSR architecture to capture relationships among objects in temporal, spatial, and regions within observation trajectories. Specifically, we implement a Temporal attention module (T) to model the temporal structure of the observation sequence, implicitly encoding historical moving or trajectory information. Then, a Spatial attention module (S) uncovers the spatial context of the current observation objects based on CRG and past observations. Last, a Region attention module (R) shifts the attention to the target-relevant region. Leveraging the visual representation extracted by our method, the agent accurately perceives the environment and easily learns a superior navigation policy. Experiments on AI2-THOR demonstrate that our CRG-TSR method significantly outperforms existing methods in both effectiveness and efficiency. The supplementary material includes the code and will be publicly available.

Tiny machine learning empowers climbing inspection robots for real-time multiobject bolt-defect detection

Ensuring the structural integrity of steel construction is critical, necessitating effective methods for detecting bolt defects. Traditional inspection methods are reliable but require significant labor and struggle with the accessibility of complex structures. This research introduces a real-time bolt-defect detection system that integrates tiny machine learning (TinyML) with a magnetic climbing robot to enhance inspection efficiency and scope. The system employs the faster objects, more objects (FOMO) algorithm optimized for edge computing on microcontrollers. This approach enables accurate bolt identification, including normal, loose, and missing bolts. The average system accuracy is 82%, with precision and recall values from 0.57 to 0.89 and 0.67 to 0.87, respectively. The system demonstrates balanced detection, evidenced by an F1 score improvement of up to 62%. The FOMO model displays compelling performance on defect detection tasks, achieving an F1 score of approximately 75%, outperforming the MobileNetV2 Single Shot Multibox Detector with Feature Pyramid Networks Lite and You Only Look Once version 5 small. The efficiency of the FOMO model is highlighted by its low hardware requirements at less than 0.1 MB of flash memory and 893.8 KB of random access memory for the 32-bit floating-point data format and is reduced for the 8-bit integer data format, with inference times of 142 ms and 86 ms, respectively. These findings contrast with the higher resource demands and slower inference time of the compared models, indicating the suitability of the FOMO model for low-capacity microcontrollers and its feasibility for real-time applications. The performance analysis across scenarios confirms the high precision (average 0.77) and recall (average 0.76), validating the robustness of the model in diverse environmental conditions. The system offers an advancement over traditional bolt-defect detection methods in accuracy and efficiency by leveraging TinyML and a magnetic climbing robot, setting new benchmarks for real-time inspection technology. The quantitative results underscore the performance of the system, presenting a scalable, cost-effective solution for improving the safety and durability of steel structures.

A general and efficient harmonic balance method for nonlinear dynamic simulation

This paper presents a general and efficient harmonic balance (GE-HB) method to expeditiously evaluate the periodic responses of nonlinear systems. By employing the fast Fourier transform (FFT) technique, it rapidly computes harmonic expansion coefficients of the nonlinear terms and their corresponding constant tensor matrices. Notably, the nonlinear part of the Jacobian matrix is just the product of the coefficients and the constant tensor matrices, loop computations are no longer requisite for calculating the Jacobian matrix. This, facilitates accelerated determination of the nonlinear portion of the Jacobian matrix, thus obtaining the periodic responses of the system in an efficient manner. Furthermore, the constant tensor matrices are exclusively contingent upon the harmonic bases of the system's periodic response, devoid of reliance on the equation's specific formulation, facilitates obtaining the Jacobian matrix in a general manner. Simulation results on a 20-degree-of-freedom nonlinear oscillator chain system demonstrate that the efficiency of the GE-HB method is about tenfold that of the harmonic balance-alternating frequency/time domain (HB-AFT) method and twenty-fivefold that of the fourth-order Runge-Kutta (RK4) method. Further, the simulation of a 284-degree-of-freedom nonlinear rotor system exhibits the GE-HB method's computational efficiency surpassing HB-AFT method by over 500 times and Newmark method by over 3000 times. Moreover, in these two examples, the GE-HB method captures all solution branches, including unstable periodic solutions, providing comprehensive insights into periodic responses of the systems. The GE-HB method proposed here has the potential to explore dynamic characteristics for high-dimensional complex nonlinear systems in engineering.

A three‐dimensional parabolic equation with fast reordered‐alternate direction decomposition in atmosphere duct environment

AbstractIn the design and optimisation of marine wireless communication and navigation systems, a thorough investigation of radio wave propagation characteristics under atmospheric ducting conditions is essential. The authors aim to enhance the efficiency of radio wave propagation loss prediction in marine atmospheric ducting environments, proposing a prediction model based on Fast Reordered‐Alternate Direction Decomposition (FR‐ADD). By approximating the diffraction term into three independent components, exploiting the commutative properties of the Fourier transform to reduce the spatial dimensions, and incorporating a rapid algorithm for the parabolic equation, the model optimises the stepping process and significantly improves computational efficiency. Simulation experiments demonstrate that, in the long‐distance and complex marine ducting environments, the model not only maintains prediction accuracy but also substantially reduces computational load and prediction time, effectively realising over‐the‐horizon propagation prediction. In the experiment of radio wave propagation characteristics in the Yellow and Bohai Seas, the simulation data from the FR‐ADD model showed significant correlation with actual measurements and simulations from the AREPS software, confirming the method's efficiency and practicality.

Optimization method of dynamic reconfiguration in virtual power plants

With the continuous expansion of distributed energy resources (DERs), virtual power plants (VPPs) have emerged as an efficient solution for their aggregation and management. This study proposes a dynamic reconfiguration method for VPPs, addressing the problem of optimally aggregating DERs within a VPP. To enhance DER participation, an innovative membership rewards mechanism is introduced. Furthermore, we develop a method for DER feature extraction and classification to deeply understand their performance. We also assess DERs' willingness to participate, which is the precondition for effective aggregation. The core of our method is an optimized strategy for multi-stage dynamic planning of VPPs, designed to accommodate the dynamic evolution of DERs and electricity markets conditions, and formulated as a two-layer model to offer optimal solutions for DERs aggregation. Numerical simulations validate the superiority of our method in economic benefits and operational efficiency of VPP, highlighting our contributions to enhance the feasibility and efficiency of VPP aggregation through multi-stage planning, dynamic adaptability, and strategic incentives.

Efficient subspace clustering and feature extraction via [formula omitted]-norm and [formula omitted]-norm minimization

The nuclear norm-based Latent Low-Rank Representation (LatLRR) has gained much attention due to its success in subspace clustering and feature extraction. However, it suffers from high computational costs due to the calculation of singular value decomposition for large matrices. To this end, we develop an efficient subspace clustering and feature extraction method (ESCFE) which substitutes the nuclear norm with the ℓ2,1-norm and ℓ1,2-norm respectively. Theoretically proof shows both the ℓ2,1-norm and ℓ1,2-norm can serve as the convex surrogates of the nuclear norm while can derive closed-form solutions. Furthermore, the ℓ2,1-norm (or ℓ1,2-norm) regularization promotes column (or row) structure sparsity due to the discriminative nature inherited from the ℓ1-norm. Thus the proposed ESCFE is robust to outliers in data and can extract features with joint sparsity. Extensive experiments on multiple benchmark datasets demonstrate the superiority of our method in both efficiency and effectiveness.

Assessing the Feasibility of Removing Graffiti from Railway Vehicles Using Ultra-Freezing Air Projection

Unauthorised graffiti is a challenge in urban environments, affecting railway structures, stations, tracks, and vehicles. Inefficient cleaning methods increase the costs and downtime of railcars, limiting passenger transport. In turn, they are harmful to the operator’s health and the environment, due to the VOCs they release. This study focuses on the feasibility of dry-ice blasting, replacing carbon dioxide with ambient air as an innovative and sustainable solution to remove graffiti from rail vehicles. Experimental tests have been carried out with 13 different aerosols, controlling the temperature (<−80 °C), pressure (up to 3 bar), projection distance (0.5 cm) and exposure times (30″/1′/2′/4′/6′/8′/++). The results showed that ultra-freezing with ambient air preserved the integrity of the support materials and altered the topography, colourimetry and adhesion of the aerosols tested, achieving the total removal of one of the paints. Preliminary results suggest that ultra-freezing with ambient air could be a viable and sustainable solution for graffiti removal on railway structures, transferable to other urban environments.

Simultaneous construction strategy using two types of fluorescent markers for HVT vector vaccine against infectious bursal disease and H9N2 avian influenza virus by NHEJ-CRISPR/Cas9.

Recently, herpesvirus of turkeys (HVT), which was initially employed as a vaccine against Marek's disease (MD), has been shown to be a highly effective viral vector for producing recombinant vaccines that can simultaneously express the protective antigens of multiple poultry diseases. Prior to the development of commercial HVT-vectored dual-insert vaccines, the majority of HVT-vectored vaccines in use only contained a single foreign gene and were often generated using time-consuming and inefficient traditional recombination methods. The development of multivalent HVT-vectored vaccines that induce simultaneous protection against several avian diseases is of great value. In particular, efficacy interference between individual recombinant HVT vaccines can be avoided. Herein, we demonstrated the use of CRISPR/Cas9 gene editing technology for the insertion of an IBDV (G2d) VP2 expression cassette into the UL45/46 region of the recombinant rHVT-HA viral genome to generate the dual insert rHVT-VP2-HA recombinant vaccine. The efficacy of this recombinant virus was also evaluated in specific pathogen-free (SPF) chickens. PCR and sequencing results showed that the recombinant virus rHVT-VP2-HA was successfully constructed. Vaccination with rHVT-VP2-HA produced high levels of specific antibodies against IBDV (G2d) and H9N2/Y280. rHVT-VP2-HA can provide 100% protection against challenges with IBDV (G2d) and H9N2/Y280. These results demonstrate that rHVT-VP2-HA is a safe and highly efficacious vaccine for the simultaneous control of IBDV (G2d) and H9N2/Y280.

Privacy-Enhanced Cooperative Storage Scheme for Contact-Free Sensory Data in AIoT with Efficient Synchronization

The growing popularity of contact-free smart sensing has contributed to the development of the Artificial Intelligence of Things (AIoT). The contact-free sensory data has great potential to mine and analyze the hidden information for AIoT-enabled applications. However, due to the limited storage resource of contact-free smart sensing devices, data is naturally stored in the cloud, which is at risk of privacy leakage. Cloud storage is generally considered insecure. On one hand, the openness of the cloud environment makes the data easy to be attacked, and the complex AIoT environment also makes the data transmission process vulnerable to the third party. On the other hand, the Cloud Service Provider (CSP) is untrusted. In this article, to ensure the security of data from contact-free smart sensing devices, a Cloud-Edge-End cooperative storage scheme is proposed, which takes full advantage of the differences in the cloud, edge, and end. Firstly, the processed sensory data is stored separately in the three layers by utilizing well-designed data partitioning strategy. This scheme can increase the difficulty of privacy leakage in the transmission process and avoid internal and external attacks. Besides, the contact-free sensory data is highly time-dependent. Therefore, combined with the Cloud-Edge-End cooperation model, this article proposes a delta-based data update method and extends it into a hybrid update mode to improve the synchronization efficiency. Theoretical analysis and experimental results show that the proposed cooperative storage method can resist various security threats in bad situations and outperform other update methods in synchronization efficiency, significantly reducing the synchronization overhead in AIoT.

Multi-scene application of intelligent inspection robot based on computer vision in power plant

As industries develop, the automation and intelligence level of power plants is constantly improving, and the application of patrol robots is also increasingly widespread. This research combines computer vision technology and particle swarm optimization algorithm to build an obstacle recognition model and obstacle avoidance model of an intelligent patrol robot in a power plant respectively. Firstly, the traditional convolutional recurrent neural network is optimized, and the obstacle recognition model of an intelligent patrol robot is built by combining the connection timing classification algorithm. Then, the artificial potential field method optimizes the traditional particle swarm optimization algorithm, and an obstacle avoidance model of an intelligent patrol robot is built. The performance of the two models was tested, and it was found that the highest precision, recall, and F1 values of the identification model were 0.978, 0.974, and 0.975. The highest precision, recall, and F1 values of the obstacle avoidance model were 0.97, 0.96, and 0.96 respectively. The two optimization models designed in this research have better performance. In conclusion, the two models in this study are superior to the traditional methods in recognition effect and obstacle avoidance efficiency, providing an effective technical scheme for intelligent patrol inspection of power plants.

Using Affine Arithmetic to Design a Controller for Large Uncertain System

This study presents a approach for creating the supervisor of expansive uncertain systems. The Controller for a particular high order system is designed using a reduced order model. The numerator and denominator polynomial in the suggested reduction approach is derived using modified polynomial differential method. A lower order model with least ISE optimization is obtained. Assuming that the initial high-order system is stable, the proposed approach guarantees the stability of the streamlined model. Through With reference to the original high-order systems, a PID controller is created for the suggested low-order model. In order to explain the method's efficiency, a few numerical examples were taken into consideration. It has been demonstrated that applying Control from the lower order model to the higher order system is improved and the controlled system's performance. Common numerical illustrations seen in the literature have been used to test the method, and the results show that it works satisfactorily.

A Systematic Review of Applications of Generative Design Methods for Energy Efficiency in Buildings

Energy efficiency is a principle of architectural design that reduces environmental impact. Generative design can offer alternative options to improve energy efficiency in buildings, but significant gaps exist in the application due to accessing complex knowledge. This study aimed to explore publications on generative design and energy efficiency in buildings and identify generative methods for energy efficiency topics. This study conducted a systematic review using the PRISMA methodology in December 2023 by searching publications from databases including Scopus, Google Scholar, and Thai Journals Online. Descriptive analysis examined 34 articles, showing the publication year, source, and citations. Comparative qualitative and descriptive analysis identified generative methods. Publications are increasing over time, and further growth is expected related to the accessibility of computational design and practical applications. Tools and frameworks demonstrated reduced energy usage compared to prototypes or traditional design approaches. The most studied is thermal performance, which was reduced by 28%. Energy performance achieved up to a 23.30% reduction, followed by others and daylighting. In addition to single-topic studies, there are also studies with multiple topics. Evolutionary algorithms are standard. Parametric search strategies have increased. Exploration reveals rule-based and mixed methods. Machine learning and AI garner attention.

The Literature Review on Curriculum Implementation Problems

Background: Changes in knowledge and technology are continually shaping the educational environment, thereby influencing curriculum implementation. Innovations are being made to meet the evolving educational goals, which includes overcoming various barriers to curriculum implementation. Objective: The aim of this study was to examine the existing literature on curriculum implementation problems, particularly focusing on the issues faced by educational institutions in effectively applying educational theories and practices. Methods: A comprehensive literature review was conducted, utilizing databases such as PubMed, ERIC, Google Scholar, and JSTOR. Articles published from 1997 to 2022 that discussed challenges and strategies in curriculum implementation across various educational levels and settings were included. Thematic analysis was employed to synthesize the data, identifying common themes related to the challenges and solutions of curriculum implementation. Results: The review identified critical challenges related to four main areas: teachers, students, the curriculum itself, and institutional factors. Teacher-related challenges included inadequate professional development and inefficient teaching methods. Student-related challenges highlighted diverse learning needs and varying levels of engagement. Institutional challenges pointed to resource limitations and insufficient stakeholder support. Curriculum-related challenges were mostly about alignment and compatibility with educational goals. Conclusion: Effective curriculum implementation is pivotal in educational settings and requires comprehensive strategies to address the myriad of challenges identified. Enhancing teacher training, improving resource allocation, and ensuring curriculum relevance are crucial for improving educational outcomes.

Drug repositioning based on tripartite cross-network embedding and graph convolutional network

Drug-disease association prediction is an important part of drug discovery, which can help researchers uncover potential drug candidates and disease targets more accurately to deal with the drawbacks of inefficiency and high cost of traditional methods. In contrast, past drug-disease prediction processes often overlooked the associations between drugs and side effects and the impact of proteins on diseases. These issues hindered models from fully learning the potential information within the network. Hence, a method capable of comprehensively learning from multi-source heterogeneous information and extracting high-level features of biological associations is needed. To overcome these challenges, proposes a drug repositioning method based on a tripartite cross-network embedding graph convolutional network, called TGCNDR, to predict the associations between drugs and diseases. Firstly, we construct a tripartite cross-network of drug-disease associations, drug-protein associations, and drug-side effect associations. TGCNDR employs graph convolutional networks to capture information from different nodes to learn drug embedding. Subsequently, TGCNDR uses anchor links for knowledge transfer, and different networks use the Self-attention mechanism to represent drug importance. Experiments on benchmark datasets demonstrate that TGCNDR exhibits good performance (the average AUROC is 0.922, the average AUPR is 0.916, and the average F1-score is 0.860) compared with state-of-the-art methods. In the case study, we perform molecular docking experiments on the predicted drug candidates and identify newly approved drugs for osteosarcoma, which suggests that TGCNDR is an effective tool for drug repositioning.

Optimization for industrial robot joint movement in non-horizontal 3D printing application

When a robot is printing a sequence of non-horizontal goal poses, its joint values often undergo significant variations, resulting in challenges such as singularities or exceeding joint limits. This paper proposes two new methods aimed at optimizing goal poses to solve the problem. The first method, employing an analytical approach, modifies the goal poses to maintain the 4th joint value of a 6-axis industrial robot at zero. This adjustment effectively reduces the motion range of the 5th and 6th axes. The second method utilizes numerical optimization to adjust the goal poses, aiming to minimize the motion range of all joints. Leveraging the analytical method to obtain one good initial value, numerical optimization is subsequently applied to complete the entire path optimization, creating an optimization workflow. It is also possible to use only analytical methods for computational efficiency. The feasibility and effectiveness of these two methods are validated through simulation and real project case.

Adaptive Random Mac Strategy for IoT Security Through Network Forensics Investigation

In the rapidly evolving landscape of digital systems and networks, ensuring robust cyber security measures is of paramount importance. This study delves into the evaluation of a proposed security method's efficiency through a comprehensive comparative analysis conducted under diverse security breach simulations. The objective is to discern the method's effectiveness in safeguarding sensitive information and maintaining system integrity.The research methodology involves the selection of representative security breach scenarios, each emulating distinct attack vectors and potential vulnerabilities. A benchmark security protocol, widely acknowledged for its reliability, serves as a reference for the proposed method's assessment. A series of controlled breach simulations are executed, simulating real-world cyber threats, ranging from sophisticated malware intrusions to social engineering attacks. Key performance metrics, such as response time, resource utilization, and data recovery rates, are meticulously recorded and analyzed for both the benchmark and proposed methods. Furthermore, the impact on system functionality and user experience is evaluated to comprehend the trade-off between heightened security and seamless user interaction. The experiments are conducted across multiple system architectures to account for variations in hardware and software configurations. The findings reveal nuanced insights into the proposed method's efficiency under distinct breach scenarios. It showcases commendable resilience against certain attack vectors while potentially exposing limitations in others. The comparative analysis provides a comprehensive overview of the method's overall efficacy and enables the identification of scenarios where enhancements might be necessary. The performance of the suggested approach under various security breach simulations is empirically demonstrated in this paper, adding to the body of current knowledge. The insights gained from this research aid in refining the method's design and implementation, bolstering its potential to address the dynamic and evolving landscape of cyber security threats. As digital systems continue to play an increasingly integral role in our lives, the significance of such evaluations cannot be overstated, ensuring that our vital digital infrastructure remains secure and resilient.

Application of Developmental Regulators for Enhancing Plant Regeneration and Genetic Transformation.

Establishing plant regeneration systems and efficient genetic transformation techniques plays a crucial role in plant functional genomics research and the development of new crop varieties. The inefficient methods of transformation and regeneration of recalcitrant species and the genetic dependence of the transformation process remain major obstacles. With the advancement of plant meristematic tissues and somatic embryogenesis research, several key regulatory genes, collectively known as developmental regulators, have been identified. In the field of plant genetic transformation, the application of developmental regulators has recently garnered significant interest. These regulators play important roles in plant growth and development, and when applied in plant genetic transformation, they can effectively enhance the induction and regeneration capabilities of plant meristematic tissues, thus providing important opportunities for improving genetic transformation efficiency. This review focuses on the introduction of several commonly used developmental regulators. By gaining an in-depth understanding of and applying these developmental regulators, it is possible to further enhance the efficiency and success rate of plant genetic transformation, providing strong support for plant breeding and genetic engineering research.

Determination of trace levels of thiazopyr residues in oolong tea samples using metal sieve-linked double syringe based liquid-phase microextraction coupled with gas chromatography-mass spectrometry

Thiazopyr has raised concerns about its potential impact on human health. Hence, detection of trace levels of thiazopyr herbicide has become critically important. This study presents a highly precise and accurate method to determine thiazopyr at trace levels using a combination of metal sieve-linked double syringe based liquid-phase microextraction (MSLDS-LPME) and Gas Chromatography-Mass Spectrometry (GC-MS) system. A thorough optimization of all parameters affecting the method's efficiency was performed using a univariate optimization approach, followed by an evaluation of the system's analytical performance. Employing the developed method in conjunction with the MSLDS-LPME-GC-MS system yielded a limit of detection (LOD) of 2.73 µg/kg and a limit of quantification (LOQ) of 9.11 µg/kg. To assess the method's applicability, a recovery study was conducted using oolong tea as a real sample matrix, employing a matrix-matching calibration approach. The results of recovery studies demonstrated acceptable recoveries ranging from 87.5 % to 101.9 %.

Exploring Data Input Problems in Mixed Reality Environments: Proposal and Evaluation of Natural Interaction Techniques

Data input within mixed reality environments poses significant interaction challenges, notably in immersive visual analytics applications. This study assesses five numerical input techniques: three benchmark methods (Touch-Slider, Keyboard, Pinch-Slider) and two innovative multimodal techniques (Bimanual Scaling, Gesture and Voice). An experimental design was employed to compare these techniques’ input efficiency, accuracy, and user experience across varying precision and distance conditions. The findings reveal that multimodal techniques surpass slider methods in input efficiency yet are comparable to keyboards; the voice method excels in reducing cognitive load but falls short in accuracy; and the scaling method marginally leads in user satisfaction but imposes a higher physical load. Furthermore, this study outlines these techniques’ pros and cons and offers design guidelines and future research directions.

Vitamin Deficiency Detection from eye Using Machine Learning

Vitamin deficiencies affect millions globally, with over two billion people at risk. Early detection is crucial, yet one in three youngsters lacks proper vitamin intake. Addressing this, a novel machine learning-based approach, using eye images instead of blood samples, offers a free desktop application for vitamin deficiency detection. The software, trained to distinguish healthy and deficient eyes, provides users with comprehensive reports. Early identification of deficiencies can prevent anemia, infectious illnesses, and developmental issues. Real-world trials confirm the method's superior efficiency compared to previous approaches, marking a promising advancement in global health. Keywords: Machine Learning, Opthalmology, YOLO, Deep Learning, Image preprocessing , extraction, ultralytics.