Search

The use of object detection methods is becoming increasingly important in the internet of smart energy. Accurate detection of power line region and suspicious objects can reduce the probability of accidents and save manpower. However, the noisy outdoor background and time-varying lighting conditions bring difficulties to those tasks. To address those challenges, this paper investigates mainstream methods of power line region surveillance and proposes a new method for improving the accuracy of power line region and risk object detection by modeling the heat map branch of CenterNet, which is the most representative of anchor-free detectors, with a cascade guiding structure and an improved loss function. Finally, comprehensive experiments are conducted to compare some state-of-the-art detectors and the proposed model on power line surveillance tasks. The test results demonstrate both the effectiveness and superiority of the proposed method.

The online monitoring of water environments is urgently needed. A feasible and effective approach is the use of agents. Water environments, similar to other real-world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, agents should be prepared to deal with various situations. In this study, we focused on an adaptive agent tracking approach for oil contamination. An integrated tracking framework, which is used to track the moving contour of oil pollution via a system comprising multiple unmanned surface vehicles, is proposed. The zigzag, unmanned underwater vehicle-gas, cloverleaf trajectory and curvature-weighted deployment algorithm methods are employed with consideration of their suitability to our approach. A cyclic particle swarm optimisation–Kalman method is also proposed. The possible position of moving vertices is predicted by the Kalman filter, and an objective search region is generated around the centre position. Moreover, particle swarm optimisation is performed to search for the best target position in this region. This particle swarm optimisation–Kalman method is circle operated to compensate for the deficiency of a few agents. To evaluate the approach, we conduct usability and performance simulations.

Online monitoring and water quality analysis of lakes are urgently needed. A feasible and effective approach is to use a Wireless Sensor Network (WSN). Lake water environments, like other real world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, the WSN node has to be prepared to deal with varying situations. This paper presents a WSN self-configuration approach for lake water quality monitoring. The approach is based on the integration of a semantic framework, where a reasoner can make decisions on the configuration of WSN services. We present a WSN ontology and the relevant water quality monitoring context information, which considers its suitability in a pervasive computing environment. We also propose a rule-based reasoning engine that is used to conduct decision support through reasoning techniques and context-awareness. To evaluate the approach, we conduct usability experiments and performance benchmarks.

Research on smart homes (SHs) has increased significantly in recent years because of the convenience provided by having an assisted living environment. The functions of SHs as mentioned in previous studies, particularly safety services, are seldom discussed or mentioned. Thus, this study proposes a semantic approach with decision support for safety service in SH management. The focus of this contribution is to explore a context awareness and reasoning approach for risk recognition in SH that enables the proper decision support for flexible safety service provision. The framework of SH based on a wireless sensor network is described from the perspective of neighbourhood management. This approach is based on the integration of semantic knowledge in which a reasoner can make decisions about risk recognition and safety service. We present a management ontology for a SH and relevant monitoring contextual information, which considers its suitability in a pervasive computing environment and is service-oriented. We also propose a rule-based reasoning method to provide decision support through reasoning techniques and context-awareness. A system prototype is developed to evaluate the feasibility, time response and extendibility of the approach. The evaluation of our approach shows that it is more effective in daily risk event recognition. The decisions for service provision are shown to be accurate.

The molecular structure of the title compound, [Al(CH3)2(C22H26NO)], displays a monomer with the AlIII atom in a distorted tetrahedral environment defined by two methyl groups and the N and O atoms of the chelating ketiminate anion. The O—Al—N bite angle of the chelating ligand is 94.14 (9)°. The O—C—C—C—N backbone of the ligand is nearly coplanar (r.m.s. deviation = 0.029 Å) and the Al atom deviates significantly from the mean plane by 0.525 (3) Å. In the crystal, weak intermolecular C—H...O interactions are observed.

Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting-edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic gradient descent, logistic regression, random forest, decision trees, k-nearest neighbor, and naive Bayes. The system was trained using Common Objects in Context (COCO), the most challenging publicly available dataset. Notably, a yearly object detection challenge is held using COCO. The resulting technology is quick and precise, making it ideal for applications requiring an object detection accuracy of 97%. [ABSTRACT FROM AUTHOR]

Abstract The d-transition kagome metals provide a novel platform for exploring correlated superconducting state intertwined with charge ordering. However, the force of charge-density-wave (CDW) and superconductivity (SC) formation, and the mechanism underlying electron pairing remain elusive. Here, utilizing our newly developed methodology based on electride states as fingerprints, we propose a novel class of hydrogen-kagome superconductors AH3Li5 (A = C, Si, P) with ideal kagome band characteristics and elucidate the electron-phonon coupling (EPC) mechanism responsible for electron pairing. The representative compressed PH3Li5 and CH3Li5 demonstrates impressive superconducting transition temperatures (T cs) of 120.09 K and 57.18 K, respectively. Importantly, the CDW competes with SC thus resulting in a pressure-driven dome-shaped SC in CH3Li5, where the CDW order was induced by both EPC and Fermi surface nesting. Our study presents a scientific method for identifying high-T c hydrogen-kagome metals and provides new avenues to fundamentally understand the underlying mechanism of CDW and SC, thereby guiding future experimental investigations.