Minimizing the combined effect of the weighted average completion delay and average energy consumption of users forms the objective function, a mixed-integer nonlinear problem. We introduce an enhanced particle swarm optimization algorithm (EPSO) as an initial step in the optimization of the transmit power allocation strategy. The Genetic Algorithm (GA) is then applied to refine the subtask offloading strategy. We introduce an alternative optimization approach, EPSO-GA, to collaboratively optimize transmit power allocation and subtask offloading strategies. Simulation data show the EPSO-GA algorithm achieving better performance than competing algorithms in lowering the average completion delay, average energy consumption, and average cost. The lowest average cost is consistently achieved by the EPSO-GA algorithm, regardless of how the importance of delay and energy consumption is balanced.
High-definition imagery of entire large-scale construction sites is becoming increasingly important for monitoring management tasks. Yet, the transmission of high-definition images constitutes a major problem for construction sites facing harsh network environments and insufficient computing resources. For this reason, a high-performance compressed sensing and reconstruction method is required for high-definition monitoring images. While deep learning-based image compressed sensing methods demonstrably outperform traditional approaches in reconstructing images from limited measurements, significant challenges persist in delivering high-definition, accurate, and efficient compression on large construction sites while also minimizing memory usage and computational load. This research investigated the performance of an efficient deep-learning framework (EHDCS-Net) for high-definition image compressed sensing applications in large-scale construction site monitoring. The framework's architecture consists of four primary components: sampling, initial recovery, deep recovery, and recovery output. Through a rational organization of the convolutional, downsampling, and pixelshuffle layers, based on block-based compressed sensing procedures, this framework was exquisitely designed. The framework employed nonlinear transformations on reduced feature maps during image reconstruction, thus achieving significant reductions in memory usage and computational cost. The addition of the ECA (efficient channel attention) module served to increase the nonlinear reconstruction capacity for reduced-resolution feature maps. Testing of the framework was carried out on large-scene monitoring images derived from a real hydraulic engineering megaproject. Comparative experimentation highlighted that the EHDCS-Net framework's superior reconstruction accuracy and faster recovery times stemmed from its reduced memory and floating-point operation (FLOPs) requirements compared to current deep learning-based image compressed sensing methods.
The process of detecting pointer meter readings by inspection robots in intricate environments is susceptible to reflective phenomena, a factor that can result in reading failures. Utilizing deep learning, this paper develops an enhanced k-means clustering approach for adaptive reflective area detection in pointer meters, accompanied by a robotic pose control strategy aimed at removing those regions. To achieve the objective, three steps are followed. The first step involves utilizing a YOLOv5s (You Only Look Once v5-small) deep learning network to accomplish real-time detection of pointer meters. Preprocessing of the detected reflective pointer meters involves the application of a perspective transformation. The detection results and the deep learning algorithm are subsequently merged and then integrated with the perspective transformation. The collected pointer meter images' YUV (luminance-bandwidth-chrominance) color spatial information provides the data necessary for creating the fitting curve of the brightness component histogram, and identifying its peak and valley characteristics. Based on this information, the k-means algorithm is further developed, leading to the adaptive determination of its optimal clustering number and initial cluster centers. Moreover, pointer meter image reflection detection is accomplished using a refined k-means clustering approach. The reflective areas can be avoided by strategically controlling the robot's pose, considering both its moving direction and travel distance. The proposed detection methodology is finally tested on an inspection robot detection platform, allowing for experimental assessment of its performance. Through experimentation, it has been found that the proposed algorithm achieves a notable detection accuracy of 0.809 while also attaining the quickest detection time, only 0.6392 seconds, when evaluated against other methods previously described in academic literature. MEK162 Inspection robots can benefit from this paper's theoretical and technical framework, which aims to mitigate circumferential reflections. The inspection robots' movement is precisely controlled to quickly remove the reflective areas on pointer meters, with adaptive precision. Real-time reflection detection and recognition of pointer meters for inspection robots operating in complex environments is a potential application of the proposed detection method.
Multiple Dubins robots' coverage path planning (CPP) has seen widespread use in aerial monitoring, marine exploration, and search and rescue operations. In multi-robot coverage path planning (MCPP) research, coverage issues are tackled using precise or heuristic algorithms. Exact algorithms focusing on precise area division typically outperform coverage-based methods. Conversely, heuristic approaches encounter the challenge of balancing the desired degree of accuracy with the substantial demands of the algorithm's computational complexity. The Dubins MCPP problem, in environments with known characteristics, forms the core of this paper's focus. MEK162 Using mixed linear integer programming (MILP), we formulate and present the EDM algorithm, an exact Dubins multi-robot coverage path planning method. The EDM algorithm's search covers the full solution space to identify the optimal shortest Dubins coverage path. Presented next is a heuristic, approximate credit-based Dubins multi-robot coverage path planning (CDM) algorithm. The algorithm employs a credit model to balance tasks among robots and a tree-partitioning strategy to manage computational overhead. Benchmarking EDM against other exact and approximate algorithms indicates that EDM achieves the least coverage time in compact scenes; conversely, CDM delivers faster coverage times and reduced computation times in extensive scenes. Applying EDM and CDM to a high-fidelity fixed-wing unmanned aerial vehicle (UAV) model demonstrates their applicability, as shown by feasibility experiments.
Early diagnosis of microvascular changes associated with COVID-19 could provide a significant clinical opportunity. The analysis of raw PPG signals, captured by pulse oximeters, served as the basis for this study's aim: to define a deep learning approach for the identification of COVID-19 patients. A finger pulse oximeter was utilized to collect PPG signals from 93 COVID-19 patients and 90 healthy control subjects, thereby enabling the development of the method. We designed a template-matching method to identify and retain signal segments of high quality, eliminating those affected by noise or motion artifacts. A custom convolutional neural network model was subsequently developed using these samples as a foundation. The model receives PPG signal segments as input and performs a binary classification, distinguishing COVID-19 cases from control groups. The proposed model's performance in identifying COVID-19 patients, as assessed through hold-out validation on test data, showed 83.86% accuracy and 84.30% sensitivity. Photoplethysmography emerges as a potentially valuable instrument for evaluating microcirculation and promptly identifying SARS-CoV-2-linked microvascular alterations, as the results demonstrate. Subsequently, a non-invasive and inexpensive methodology is remarkably well-suited for the development of a user-friendly system, potentially functioning effectively even in settings with resource-limited healthcare.
For twenty years, a research group composed of individuals from various universities in Campania, Italy, has pursued the study of photonic sensors for enhancing safety and security in healthcare, industrial, and environmental applications. This paper marks the commencement of a trio of interconnected articles, highlighting the preliminary groundwork. This paper outlines the fundamental principles behind the photonic technologies used in our sensor development. MEK162 Next, we scrutinize our core results pertaining to the innovative applications of infrastructure and transportation monitoring.
The widespread adoption of distributed generation (DG) within distribution networks (DNs) mandates improved voltage control techniques for distribution system operators (DSOs). Power flow increases resulting from the deployment of renewable energy plants in unpredicted sections of the distribution network can affect voltage profiles, potentially leading to outages at secondary substations (SSs) with voltage limit transgressions. Widespread cyberattacks on critical infrastructure, occurring concurrently, present novel challenges for DSOs' security and dependability. This paper delves into the impact of injected false data from residential and non-residential clients on a centralized voltage regulation scheme, requiring distributed generation units to dynamically adapt their reactive power exchanges with the grid according to the voltage profile. According to field data, the centralized system predicts the distribution grid's state and generates reactive power requirements for DG plants, thereby preempting voltage infringements. To establish a false data generation algorithm, a preliminary analysis of false data is executed in the context of the energy industry. Afterward, a customizable false-data generation instrument is constructed and employed. The impact of increasing distributed generation (DG) penetration on false data injection within the IEEE 118-bus system is investigated. An analysis of the effects of injecting false data into the system reveals a critical weakness in the security frameworks of Distribution System Operators (DSOs), necessitating stronger safeguards to prevent significant power outages.