Search results

Unlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.

The paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.

Simulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.

Conventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

This study objective is twofold. This study aims to present an institutional analysis of the implications of job localization programs in Gulf Cooperation Council (GCC) countries, such as Saudi Arabia, United Arab Emirates and Qatar. Further, it highlights the impacts of these programs on the accounting profession.

This study is based primarily on the desktop research method, where data is collected from the review of previous studies, published data on Internet Websites and reports released by International organizations such as the United Nations. In addition, the study benefitted from conducting six interviews with government officials from GCC countries. Theoretically, this study draws upon insights from the institutional logics theory to discern higher-order institutions deriving job localization decisions in the GCC region.

This paper explained how job localization policies in the GCC region are informed by three central logics: economic, socio-political and professional. Despite contributing to achieving some socio-political goals for policymakers, these policies could have serious consequences for the practice of the professions and, hence, the local business environment. Besides, this paper highlighted the serious localization policies' impacts on the accounting profession, especially the quality of the workforce (accountants) and their job readiness.

This study highlights the various implications of job localization policies for locals, foreigners, public and private sector entities and governments. Besides, it has recommended some actions to mitigate the negative influences of such policies on the surrounding society.

This study contributes to the literature by following an interpretative approach in explaining the localization of the accounting profession from an institutional perspective by bringing new evidence from GCC emerging markets.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting.

Using a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning.

Improvisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting.

This study aims to identify the location of cracks in composite plates using a normalized mode shape curve algorithm. Crack in any structure is a destructive occurrence. Detecting these cracks early is pivotal for ensuring safety and preventing potential accidents. To prevent failure of structures, it is crucial to detect these cracks effectively and take the necessary precautions. Hence, crack detection and localization techniques are used to avoid sudden failures of structures while in operation.

An experimental modal analysis is conducted on composite plates with and without cracks to determine the natural frequencies and mode shapes. For this purpose, an impact hammer, uniaxial accelerometer and four-channel vibration analyzer are used to find the natural frequencies and mode shapes. Numerical modal analysis is performed on no crack and cracked composite plates using ANSYS software, and these are validated by the experimental modal analysis results. The normalized mode shapes algorithm is trained using test data of the first three natural frequencies collected from numerical modal analysis on different cracked composite plates for localization of crack.

The natural frequencies derived from both experimental modal analysis and numerical modal analysis exhibit a variance of 9.6%. The estimation of the crack location is achieved with exceptional precision by intersecting the first three normalized mode shapes. The first three normalized mode shape curve intersections provide a solid indication of the crack’s location. As the difference in error between the actual and estimated crack locations is only 0.9%.

This study introduces the first application of experimental modal analysis in conjunction with the normalized mode shape curve algorithm for localizing cracks in composite plates. The normalization process of mode shapes, derived from experimental modal analysis, forms a fundamental component of the mode shape curve algorithm specifically designed for crack localization. Combining experimental modal analysis with a specific algorithm of normalizing mode shapes is used to identify and locate cracks within these composite plates.

Digital voice assistants (DVAs) are revolutionising consumers’ interactions with technology and businesses. Whilst research on the adoption of these devices is rapidly expanding, few have explored post-adoption behaviour. To fill this gap, we investigate how functionality and human-like features shape customers’ emotions, engagement and loyalty towards DVAs.

The data were collected through a self-administered online survey from 509 DVA users. Structural equation modelling was employed for data analysis.

The results reveal that distinct human-like and functional factors of DVA independently explain customers’ positive emotions and engagement with DVAs. Positive emotions and engagement significantly impact customer loyalty to DVAs. The study shows that localisation of DVAs has a significant positive moderating influence on the service experience-customer engagement relationship but a negative moderating influence on the anthropomorphism-customer engagement relationship.

Unlike previous research, this study contributes to the literature by delving into post-adoption phenomena. It explains how DVAs’ human-like and functional attributes drive customers’ positive emotional responses, engagement and loyalty towards DVAs. The findings not only unveil new insights into the moderating role of localisation but also provide a crucial understanding regarding the boundary conditions of the influence of anthropomorphism and service experience on customer engagement.

The Operations and Maintenance (O&M) cost of a facility is typically 60–85% of the total life cycle cost of a building whereas its design and construction cost accounts for only 5–10%. Therefore, enhancing and optimizing the O&M of a facility is a crucial issue. In addition, with the increasing complexities in a building's operating systems, more technologically advanced solutions are required for proactively maintaining a facility. Thereby, a tool is needed which can optimize and reduce the cost of facility maintenance. One of the solutions is Augmented or Mixed Reality (AR/MR) technologies which can reduce repair time, training time and streamline inspections. Therefore, the purpose of this study is to establish contextual knowledge of AR/MR application in facilities operation and maintenance and present an implementation framework through the analysis and classification of articles published between 2015 and 2022.

To effectively understand all AR/MR applications in facilities management (FM), a systematic literature review is performed. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol was followed for searching and describing the search strategies. Keywords were identified through the concept mapping technique. The Scopus database and Google Scholar were employed to find relevant articles, books and conference papers. A thorough bibliometric analysis was conducted using VOS Viewer and subsequently, a thematic analysis was performed for the selected publications.

The use of AR/MR within facilities O&M could be categorized into five different application areas: (1) visualization; (2) maintenance; (3) indoor localization and positioning; (4) information management and (5) indoor environment. After a thematic analysis of the literature, it was found that maintenance and indoor localization were the most frequently used research application domains. The chronological evolution of AR/MR in FM is also presented along with the origin of publications, which showed that the technology is out of its infancy stage and is ready for implementation. However, literature showed many challenges hindering this goal, that is (1) reluctance of the organizational leadership to bear the cost of hardware and trainings for the employees, (2) Lack of BIM use in FM and (3) system lagging, crashing and unable to register the real environment. A preliminary framework is presented to overcome these challenges.

This study accommodates a variety of application domains within facilities O&M. The publications were systematically selected from the existing literature and then reviewed to exhibit various AR/MR applications to support FM. There have been no literature reviews that focus on AR and/or MR in the FM and this paper fills the gap by not only presenting its applications but also developing an implementation framework.

Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios.

The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM.

The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods.

The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.

In response to the navigation challenges faced by coal mine tunnel inspection robots in semistructured underground intersection environments, many current studies rely on structured map-based planning algorithms and trajectory tracking techniques. However, this approach is highly dependent on the accuracy of the global map, which can lead to deviations from the predetermined route or collisions with obstacles. To improve the environmental adaptability and navigation precision of the robot, this paper aims to propose an adaptive navigation system based on a two-dimensional (2D) LiDAR.

Leveraging the geometric features of coal mine tunnel environments, the clustering and fitting algorithms are used to construct a geometric model within the navigation system. This not only reduces the complexity of the navigation system but also optimizes local positioning. By constructing a local potential field, there is no need for path-fitting planning, thus enhancing the robot’s adaptability in intersection environments. The feasibility of the algorithm principles is validated through MATLAB and robot operating system simulations in this paper.

The experiments demonstrate that this method enables autonomous driving and optimized positioning capabilities in harsh environments, with high real-time performance and environmental adaptability, achieving a positioning error rate of less than 3%.

This paper presents an adaptive navigation system for a coal mine tunnel inspection robot using a 2D LiDAR sensor. The system improves robot attitude estimation and motion control accuracy to ensure safe and reliable navigation, especially at tunnel intersections.

The human-following task is a fundamental function in human–robot collaboration. It requires a robot to recognize and locate a target person, plan a path and avoid obstacles. To enhance the applicability of the human-following task in various scenarios, it should not rely on a prior map. This paper aims to introduce a human-following method that meets these requirements.

For the identification and localization of the target person (ILTP), this paper proposes an approach that integrates data from a camera, a light detection and ranging (LiDAR) and a ultra-wideband (UWB) anchor. For path planning and obstacle avoidance, a modified timed-elastic-bands (TEB) algorithm is introduced.

Compared to the UWB-only method, where only UWB is used to locate the target person, the proposed ILTP method in this paper reduces the localization error by 41.82%. Experimental results demonstrate the effectiveness of the ILTP and the modified TEB method under various challenging conditions. Such as crowded environments, multiple obstacles, the target person being occluded and the target person moving out of the robot’s field of view. The complete experimental videos are available for viewing on https://youtu.be/ZKbrNE1sePM.

This paper offers a novel solution for human-following tasks. The proposed ILTP method can recognize the target person among multiple individuals, determine whether the target person is lost and publish the target person’s position at a frequency of 20 Hz. The modified TEB algorithm does not rely on a prior map. It can plan paths and avoid obstacles effectively.

Traditional visual simultaneous localization and mapping (SLAM) systems are primarily based on the assumption that the environment is static, which makes them struggle with the interference caused by dynamic objects in complex industrial production environments. This paper aims to improve the stability of visual SLAM in complex dynamic environments through semantic segmentation and its optimization.

This paper proposes a real-time visual SLAM system for complex dynamic environments based on YOLOv5s semantic segmentation, named YLS-SLAM. The system combines semantic segmentation results and the boundary semantic enhancement algorithm. By recognizing and completing the semantic masks of dynamic objects from coarse to fine, it effectively eliminates the interference of dynamic feature points on the pose estimation and enhances the retention and extraction of prominent features in the background, thereby achieving stable operation of the system in complex dynamic environments.

Experiments on the Technische Universität München and Bonn data sets show that, under monocular and Red, Green, Blue - Depth modes, the localization accuracy of YLS-SLAM is significantly better than existing advanced dynamic SLAM methods, effectively improving the robustness of visual SLAM. Additionally, the authors also conducted tests using a monocular camera in a real industrial production environment, successfully validating its effectiveness and application potential in complex dynamic environment.

This paper combines semantic segmentation algorithms with boundary semantic enhancement algorithms to effectively achieve precise removal of dynamic objects and their edges, while ensuring the system's real-time performance, offering significant application value.