Search Results (7)

This study compares and analyzes statistical, machine learning, and deep learning outlier-detection methods on real power-usage data from the metal, food, and chemical industries to propose the optimal model for improving energy-consumption efficiency. In the metal industry, a Z-Score-based statistical approach with threshold optimization was used; in the food industry, a hybrid model combining K-Means, Isolation Forest, and Autoencoder was designed; and in the chemical industry, the DBA K-Means algorithm (Dynamic Time Warping Barycenter Averaging) was employed. Experimental results show that the Isolation Forest–Autoencoder hybrid delivers the best overall performance, and that DBA K-Means excels at detecting seasonal outliers, demonstrating the efficacy of these algorithms for smart energy-management systems and carbon-neutral infrastructure Full article

As a result of rapid urbanization in China, the spatial restructuring of towns and cities has significantly impacted urban compactness. The study of the spatio–temporal characteristics and driving mechanisms of urban compactness in central China is a strategic imperative and conducive to promoting regional sustainable development that is based on easing the contradiction between land resource supply and demand and reducing energy consumption. Therefore, this study focused on 80 prefecture-level cities in central China, utilizing barycenter model and GTWR model, among others, to analyze the spatio–temporal evolution pattern of urban compactness from 2006 to 2020 and its driving factors, with the aim of uncovering the intrinsic mechanisms behind enhancing urban compactness in the area. The results show the follows: (1) The urban compactness in central China has generally shown an upward trend, with a pronounced spatial clustering around provincial capital cities and the spatial changes in compactness predominantly concentrated in the north–south direction. (2) Various factors have influenced urban compactness, where government intervention and population aggregation present as bi-directional driving factors, while the effective use of land resources and high-quality industrial development, among others, present as positive driving factors. The spatio–temporal heterogeneity and agglomeration features of each driving factor are significant. (3) Further analysis indicates that the effective use of land resources is the primary factor in enhancing urban compactness, followed by technology. Therefore, we should adhere to the concept of compact cities and gradually promote the compactness of cities in central China based on the impact of the driving factors. Full article

Leaks in water distribution networks are estimated to account for up to 30% of the total distributed water; moreover, the increasing demand and the skyrocketing energy cost have made leak localization and adoption ever more important to water utilities. Each leak scenario is run on a simulation model to compute the resulting values of pressure and flows over the whole network. The values recorded by the sensors are seen as features of one leak scenario and can be considered as the signature of the leak. The key distinguishing element in this paper is to consider the entire distribution of data, representing a leak as a probability distribution. In this representation, the similarity between leaks can be captured by the Wasserstein distance. This choice matches the physics of the system as follows: the equations modeling the generation of flow and pressure data are non-linear. The signatures obtained through the simulation of a set of leak scenarios are non-linearly clustered in the Wasserstein space using Wasserstein barycenters as centroids. As a new set of measurements arrives, its signature is associated with the cluster with the closest barycenter. The location of the simulated leaks belonging to that cluster are the possible locations of the observed leak. This new framework allows a richer representation of pressure and flow data embedding both the modeling and the computational modules in a space whose elements are discrete probability distribution endowed with the Wasserstein distance. Experiments on benchmark and real-world networks confirm the feasibility of the proposed approach. Full article

On-orbit geometric calibration of satellite-borne laser based on infrared detectors is the key tool to ensure the elevation measurement accuracy, and the accuracy of on-orbit geometric calibration is directly determined by laser spots captured by detectors. Mathematical methods, such as gray-scale barycenter, are widely applied for centroid extraction of spots captured by infrared detectors and completely depend on the energy values at points measured by detectors, which have low precision and are greatly affected by the consistency of the detectors and other factors at present. Based on the above question, considering the consistency between the real laser footprint shape and spot captured by detectors, a centroid extraction method of laser spots captured by infrared detectors combining laser footprint images and detector observation data is proposed for making up this defect to some extent. First, the self-adaptive “two-step method” is used to denoise footprint images hierarchically to obtain the real shape of footprints for constraining the spots captured by detectors, and then the centroids of spots are extracted by using the energy-weighted barycenter method based on regional blocks. In the experiment, Gaofen-7 (GF-7) satellite is taken as the research object, and the proposed method, as well as the other six methods, are used for the centroid extraction of laser spots captured by detectors, the calculation of calibration parameters based on the single-beam and dual-beam laser calibration models, the positioning of laser footprints, and cross verification. According to the results, the plane accuracy of centroid extraction using the proposed method is as follows: 0.34 grids for Beam 1 and 0.33 grids for Beam 2. In addition, on flat terrain, the elevation accuracy of Beam 1 and Beam 2 in 2021 is 5.2 cm and 5.0 cm, respectively, 0.6 cm and 4.2 cm higher than those in the most accurate one among other methods; the elevation accuracy in 2020 is 23.3 cm and 7.1 cm, respectively, 7.7 cm and 2.7 cm higher than those in the most accurate one among other methods. On slopes and gentle slopes, the method proposed is also superior to other methods. Since the change of pointing angle caused by satellite jitter, atmosphere, etc., between different years, the accuracy drops when laser footprints of 2020 are located using the parameters of 2021. In summary, under different terrains and years, the results fully demonstrate the effectiveness and accuracy of the proposed method, which has more significant advantages than other traditional methods. Full article

This paper proposes an energy-barycenter-based waveform centroid algorithm (EWCA) for a high-precision Lidar ranging system. Firstly, the emission and echo pulse models of the pulse Lidar ranging system are established. Secondly, based on analyzing the merits and demerits of the conventional waveform centroid algorithm (CWCA) and intensity-weighted waveform centroid discrimination algorithm (IWCD). Moreover, combined with the characteristics of the energy moment distribution, the adaptive strategy is used to select the point with the higher signal as the calculation time series, and we proposed the EWCA to calculate the timing moment. Finally, we compared EWCA with CWCA and IWCD through simulation and actual experiments. The experimental simulation results show EWCA has higher accuracy and robustness than the comparison algorithm with different SNR. EWCA can achieve an average error of 0.1235 ns, a standard deviation of 0.0848 ns, and variance of 0.0072 ns at an SNR of 5 dB. At the same time, the Lidar ranging system is established to compare these methods further, and the ranging error of the proposed method can be within 20 mm when the measured distance is 40 m. This method has higher timing accuracy and application range, which has the potential to handle further ranging tasks. Full article

Underwater vehicles generate hydrodynamic wakes within a large area that last for a longtime during navigation, thus generating induced magnetic fields, and these are of great significance for detecting and tracking underwater vehicles. In combination with the wakefield and magnetic field simulations, this study adopts the dynamic overlapping mesh technology to conduct a numerical simulation of the wake magnetic field during the movement of an underwater vehicle. This paper introduces the causes of formation and laws of evolution of the wake magnetic field, analyzes its spatial distribution and time-domain changes, and discusses the time-frequency domain characteristics at different monitoring points as well as the effects of navigation speed and acceleration on wake magnetic fields. Our results indicate that the wake magnetic field of an underwater vehicle belongs to a low-frequency weak signal of 0–5 Hz; as the navigation speed increases, the barycenter frequency of the wake magnetic field decreases and the half-energy bandwidth increases. The increase in acceleration of the underwater vehicle will cause a higher growth rate of the wake magnetic field. This paper provides a theoretical reference for the detection of underwater vehicles based on wake magnetic fields. Full article

With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady. Full article