Complex Spatial Characteristics Research Articles

Potential of Hydrogen and Thermal Storage in the Long-term Transition of the Power Sector: A Case Study of China

Hydrogen and thermal storage can reduce cost of long-term and large-scale energy storage with high efficiency and low or even zero carbon emissions. Their potential in the low-carbon transition pathway of an energy system with rapid growth of energy demand, large shifting of energy supply structure and limited investment budget remains unclear. A long-term power generation planning model is proposed in this paper, featuring detailed technical and economic characteristics of hydrogen and thermal storage. The power supply system of China is selected as a case study, due to its urgent need for low-carbon transition and complex spatial characteristics. Results show that the application of hydrogen and thermal storage can benefit the development of volatile renewable power generation technologies, facilitate the transition towards zero or even negative carbon emissions while simultaneously reducing power supply costs. Hydrogen is expected to be mainly produced in spring and consumed in summer while heat is expected to be primarily generated in autumn and consumed in winter. Shifting from thermal storage to hydrogen storage might happen around 2050 in accordance to the low-carbon transition progress.

Research on fire detection method of complex space based on multi-sensor data fusion

Efficient and reliable fire detection methods are essential for addressing complex spatial characteristics of older residential communities, which often face poor fire safety conditions, various fire types, and challenges in rescue operations. In response to the problem of lacking comprehensive analysis of multidimensional data in traditional fire detection, which often leads to false alarms and missed alarms, this paper proposes a fire detection method based on multi-sensor data fusion. Various sensors are employed, including smoke sensors, temperature sensors, CO sensors, and flame sensors. These sensors collect multidimensional data, which are fused using improved adaptive filters and fuzzy Bayesian logical reasoning algorithms to establish a fire detection model based on multi-sensor data fusion. To additionally, false alarms and missed detections caused by sensor damage are further solved by exploring hidden spaces. Experimental results demonstrate that the proposed fire detection system based on multi-sensor data fusion effectively analyses fire occurrence and progression in older residential communities, and performs stability, accuracy and real-time property.

A structural depth network embedding stacking model based on Moran’s index and seasonal trend for short-term solar irradiance prediction

Solar irradiance prediction of multi-station is influenced by complex spatial characteristics and temporal fluctuations, so how to obtain accurate short-term irradiance prediction by using photovoltaic cluster data becomes a challenge. In order to address the challenge, a structural depth network embedding stacking model based on Moran’s I and seasonal trend for short-term solar irradiance prediction is proposed. Firstly, the deep spatial characteristics of photovoltaic clusters are analyzed by using bivariate Moran index to capture strong correlations between stations. Secondly, seasonal trend decomposition is used to smooth the seasonal factors in time series, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise is used to decompose the residual components to reduce the fluctuation of signals and the complexity of data feature mapping. Finally, based on the Stacking framework, Long Short-term Memory Network, Least Absolute Selection and Shrinkage Operator, Gradient Boosted Decision Tree, and Artificial Neural Network are superimposed. And the aggregated sub-modal components are introduced into the robust integration model to predict the solar irradiance in multiple steps. Through the case study of the National Renewable Energy Laboratory open-source dataset in the United States, the conclusions are as follows: (1) The spatio-temporal method in this paper can effectively extract the deep spatial characteristics of photovoltaic stations. (2) The fusion strategy of embedded structured deep network effectively improves the stacking effect. (3) Comparing with many classical forecasting models, it shows that this method can effectively improve the forecasting effect.

Measuring the Multi-Scale Landscape Pattern of China’s Largest Archipelago from a Dual-3D Perspective Based on Remote Sensing

Measuring the landscape pattern from a three-dimensional perspective is of great significance for comprehensively revealing the complex spatial characteristics of island ecosystems. However, the archipelago composed of rocky islands has received little attention as its three-dimensional landscape characteristics are difficult to quantify. This study took the largest archipelago in China, the Zhoushan Archipelago, as the study area and constructed an island landscape pattern evaluation model from a dual-three-dimensional (dual-3D) perspective. The model divided the island into upper and lower layers, namely the surface landscape based on topography and the landscape elements above the surface (i.e., vegetation and buildings), and then evaluated their landscape patterns from a three-dimensional perspective, respectively. The landscape pattern model based on a dual-3D perspective and multiple scales achieved excellent results in the archipelago. First, the island landscape pattern was evaluated from three-dimensional perspectives, including human interference, landscape fragmentation, vegetation space, and building space. Second, landscape indices such as the human interference three-dimensional index (HITI), the landscape fragmentation three-dimensional index (LFTI), the vegetation three-dimensional index (VTI), and the building three-dimensional index (BTI) established at multiple spatial scales revealed spatial heterogeneity within and between islands. Environmental factors such as elevation, slope, and island area exhibited significant correlations with them. There were significant differences in landscape pattern indices between the two-dimensional (2D) and the three-dimensional (3D) perspectives, and high values were mainly distributed in areas with significant topographic changes and larger islands. In addition, as the evaluation unit increased, the landscape indices increased, and HITI became more responsive to the transitions from 2D to 3D, while LFTI was the opposite. Therefore, the multiscale landscape pattern measurement of China’s largest archipelago based on high-resolution remote sensing was carried out from three-dimensional perspectives to accurately reveal the spatial heterogeneity.

A Novel Quantitative Approach to the Spatial Configuration of Urban Streets Based on Local Wind Environment

The urban street is an important part of the urban form and serves as the primary medium for public activities within cities. The morphological characteristics of urban street space not only influence people’s spatial experience but also have an impact on the quality of the physical environment, such as the wind environment. Numerous scholars have studied the wind environment of streets with different morphological characteristics using abstract and simplified street models, uncovering certain relationships between specific morphological features and airflow patterns. However, due to the significant disparity between simplified models and reality, researchers have attempted to develop models applicable to the complex characteristics of urban street space, with the aim of exploring the relationship between street spatial features and airflow characteristics. This study explores a method to quantify the complex spatial characteristics of urban streets, using a series of parameters to describe the streets, such as average width, proportion of secondary interfaces, and proportion of opening length. Streets with different spatial characteristics in the centre of Nanjing are selected and quantified. A comparison is conducted between the parameters representing street spatial morphology and the simulation results of the wind environment in the area, aiming to assess the effectiveness of these parameter values. The results demonstrate that complex urban spaces can still be effectively represented through parameterization and confirm the validity of our quantitative representation of complex street spatial characteristics. The results could serve as a reference for urban design and urban construction.

Enhancing Semantic Segmentation: Design and Analysis of Improved U-Net Based Deep Convolutional Neural Networks

In this research, we provide a state-of-the-art method for semantic segmentation that makes use of a modified version of the U-Net architecture, which is itself based on deep convolutional neural networks (CNNs). This research delves into the ins and outs of this cutting-edge approach to semantic segmentation in an effort to boost its precision and productivity. To perform semantic segmentation, a crucial operation in computer vision, each pixel in an image must be assigned to one of many predefined item classes. The proposed Improved U-Net architecture makes use of deep CNNs to efficiently capture complex spatial characteristics while preserving associated context. The study illustrates the efficacy of the Improved U-Net in a variety of real-world circumstances through thorough experimentation and assessment. Intricate feature extraction, down-sampling, and up-sampling are all part of the network's design in order to produce high-quality segmentation results. The study demonstrates comparative evaluations against classic U-Net and other state-of-the-art models and emphasizes the significance of hyperparameter fine-tuning. The suggested architecture shows excellent performance in terms of accuracy and generalization, demonstrating its promise for a variety of applications. Finally, the problem of semantic segmentation is addressed in a novel way. The experimental findings validate the relevance of the architecture's design decisions and demonstrate its potential to boost computer vision by enhancing segmentation precision and efficiency.

A novel method for evaluation of spatial characteristics in a gas solid separation fluidized bed

This study investigates the complex spatial characteristics of a gas-solid dense-phase fluidized bed for a separation system. A custom-designed perspex bed, equipped with multi small holes and different distributors, was used to explore gas-solid suspension characteristics. The results show that there is a certain spatial heterogeneity in the density of the gas-solid separation fluidized bed. The spatial heterogeneity of gas-solid fluidized beds is characterized by global and local dependence. An empirical model that predicts the relative solid mass flow rate was proposed. GeoDetector technology (including a factor detector, interaction detector, risk detector, and ecological detector) was first introduced to quantify the spatial characteristics of a gas-solid fluidized bed. The influence of each factor on the spatial heterogeneity of particle solid mass flow rate follows the order: axial position > gas velocity > radial position > air distributor. The interactive relationship between variables on the spatial characteristics of gas-solid separation fluidization shows a nonlinear enhancement.

Double Seismic Zones along the Eastern Aleutian-Alaska Subduction Zone Revealed by a High-Precision Earthquake Relocation Catalog

Abstract The Eastern Aleutian-Alaska Subduction Zone (EAASZ) manifests significant along-strike variations in structure and geometry. The limited spatial resolution in intermediate-depth earthquake locations precludes investigation of small-scale variations in seismic characteristics. In this study, we use an existing 3D seismic velocity model and waveform cross-correlation data to relocate the earthquakes in 2016 near the EAASZ. Our improved absolute and relative earthquake locations reveal complex spatial characteristics of double seismic zones (DSZs). There are significant variations in location, depth, layer separation, and length of the DSZs along the EAASZ. We also observe nonuniform layer separations along the slope of the subducting slab that may imply either rheological or crustal thickness variations. In addition, our results suggest a triple seismic zone (TSZ) beneath Kenai. The interplay among different factors, including dehydration of metamorphic facies, intraslab stress, preexisting structures, and abrupt changes in slab geometry, may explain the observed variations in seismogenesis of the DSZs and TSZs. The comparison of our relocated seismicity with the thermal model for the slab beneath Cook Inlet shows that the intermediate-depth earthquakes occur between 500°C and 900°C isotherms. The 2016 Mw 7.1 Iniskin earthquake and its aftershocks are located at ∼800°C–900°C. The intricate small-scale variations in different characteristics of the DSZs and intermediate-depth seismicity and their correlations with major geometrical and physical controls can provide insight into what governs the seismogenesis of subduction-induced earthquakes.

Urban land-use land-cover extraction for catchment modelling using deep learning techniques

AbstractThroughout the world, the likelihood of floods and managing the associated risk are a concern to many catchment managers and the population residing in those catchments. Catchment modelling is a popular approach to predicting the design flood quantiles of a catchment with complex spatial characteristics and limited monitoring data to obtain the necessary information for preparing the flood risk management plan. As an important indicator of urbanisation, land use land cover (LULC) plays a critical role in catchment parameterisation and modelling the rainfall–runoff process. Digitising LULC from remote sensing imagery of urban catchment is becoming increasingly difficult and time-consuming as the variability and diversity of land uses occur during urban development. In recent years, deep learning neural networks (DNNs) have achieved remarkable image classification and segmentation outcomes with the powerful capacity to process complex workflow and features, learn sophisticated relationships and produce superior results. This paper describes end-to-end data assimilation and processing path using U-net and DeepLabV3+, also proposes a novel approach integrated with the clustering algorithm MeanShift. These methods were developed to generate pixel-based LULC semantic segmentation from high-resolution satellite imagery of the Alexandria Canal catchment, Sydney, Australia, and assess the applicability of their outputs as inputs to different catchment modelling systems. A significant innovation is using the MeanShift clustering algorithm to reduce the spatial noise in the raw image and propagate it to the deep learning network to improve prediction. All three methods achieved excellent classification performance, where the MeanShift+U-net has the highest accuracy and consistency on the test imagery. The final suitability assessment illustrates that all three methods are more suitable for the parameterisation of semi-distributed modelling systems rather than the fully distributed modelling systems, where the MeanShift+U-net should be adopted for image-based impervious area extraction of urban catchment due to its superior prediction accuracy of 98.47%.

Particle flow characteristics in a gas-solid separation fluidized bed based on machine learning

Particles in a gas-solid separation fluidized bed have fluid-like properties in a fluidized state, and particle flow has a great influence on bed uniformity and stability. To study the complex spatial characteristics of particle flow, the macro flow characteristics of particles and the mass flow rate model for solid particle flow were studied with theoretical calculations and machine learning. The results showed that particle flow has obvious nonuniform characteristics in axial space. The theoretical solid mass flow rate model was then modified by introducing a bed expansion ratio. The theoretical calculation error was controlled to within 20%, and the machine learning error was within 15%. Based on these results, the machine learning method was found to be more accurate and comprehensive than the theoretical method. An artificial network model was also proposed to predict the spatial uniformity of a gas-solid fluidization system. The results showed that the accuracy of the training set was 94.9%, and that of the test set was 95.6%. Thus, the ANN model makes it possible to predict the spatial uniformity of a gas-solid fluidization system.

SEMANTIC SEGMENTATION METHOD ACCELERATED QUANTITATIVE ANALYSIS OF THE SPATIAL CHARACTERISTICS OF TRADITIONAL VILLAGES

Abstract. Rapid investigation and quantitative analysis are crucial for heritage conservation and renewal design. As an important category of architectural heritage - traditional settlements - with their large number and complex spatial characteristics, their spatial character patterns are an important support to assist settlement conservation and renewal design. However, the current means of analysis often requires manual data collection, secondary mapping of the collected data, extraction of individual elemental patterns and village boundaries. Then settlement boundary form, settlement density will be calculated by mathematical methods. The above methods are inefficient and prone to manual mapping errors, making it difficult to quantify and analyze a large number of traditional villages in a short period of time. Semantic segmentation is a computer vision technique for quickly segmenting different objects. Based on the collected remote sensing data of traditional villages, this paper established a dataset of semantic segmentation of spatial features of traditional settlements, segmenting village buildings, water systems, roads and plants. Using Transfer learning, data augmentation and other methods, a model was trained that can automatically segment elements of the villages. From the national traditional villages that have been announced so far, 60 traditional villages from different regions in the north and south were selected for analysis. The experiments show that the model established in this paper has an accuracy rate of above 86% in segmenting elements of villages, can effectively identify the location of different elements in remote sensing images, effectively improves the quantification rate of spatial features of settlements and saves the cost of mapping and data transcription. The results of the spatial characteristics of the 60 villages studied in this paper can also provide some theoretical basis and inspiration for the study, conservation, design and transformation of traditional villages.

Understanding the configuration of bus networks in urban China from the perspective of network types and administrative division effect

In recent years, the increasing segregation and fragmentation of transport service provisions, evidenced between urban and rural areas, and between different administrative divisions, has been concerned in metropolitan areas. The analysis of the spatial configuration of urban bus networks is important to understand the spatial problems and relevant mitigation solutions. Previous studies have regarded the station as the nodes in bus network analyses, while few studies have constructed bus lines as nodes through the complex network analysis. In this study, we highlighted the value of bus lines and their network types for analyzing urban bus network pattern. We developed a novel ‘line–line’ network to examine the spatial characteristics of cross-administration bus lines in a case study of Nanjing metropolitan area, China. The results suggest that the bus network exhibited a consistently bimodal node distribution in both line-line and line-node network types, though their internal mechanisms are different. By evaluating the importance of network nodes, administrative divisions were found to impact the bus network integration in the metropolitan area negatively. Community detection results further revealed that the geography of some subnetworks was affected by administrative divisions, with some exhibiting complex spatial characteristics. These insights would inform strategic development policy to improve the spatial equity and efficiency of public transport provisions in urban China.

Spatial characteristics and geographical determinants of mercury and arsenic in snow in northeastern China

Mercury (Hg) and arsenic (As) have been the focus of extensive in environmental research due to their strong enrichment and high toxicity. Although their concentrations in snow are usually considered important indicators for assessing regional and global atmospheric environments, few studies have focused on the biogeochemical cycling behavior of Hg and As in snow, especially in northeastern China. Therefore, to evaluate their local atmospheric deposition and the influence of trace element pollution on the atmosphere, this study assessed the spatial distribution characteristics and sources of snow-borne Hg and As in northeastern China, based on 86 snow samples acquired from December 2017 to January 2018. The total mercury (THg) and total arsenic (TAs) in the samples were measured using atomic fluorescence spectroscopy, their spatial distribution was determined through spatial interpolation, and their sources were analyzed using a backward trajectory model. The results clearly demonstrated that variations in air-mass source and industrial outputs lead to complex spatial characteristics of Hg and As concentrations. This study provides a reference for regional cooperation and collaboration in environment regulation and pollution prevention.

Indicator-based data assimilation with multiple-point statistics for updating an ensemble of models with non-Gaussian parameter distributions

Ground-water flow and the transport of contaminants in groundwater systems are strongly controlled by geologic heterogeneities. Because, the data available to model these subsurface heterogeneities are generally sparse, the observed groundwater flow, piezometric head at well locations, or the concentration profile of solutes provide valuable additional information about these complex subsurface systems. Regardless, there is likely to be considerable uncertainty associated with the predictions of the subsurface heterogeneities. This has motivated the development of several ensemble-based schemes for assimilating flow response data into predictions of subsurface heterogeneities. Most ensemble-based data assimilation methods including ensemble Kalman filter (EnKF) or indicator-based data assimilation (InDA), used for assimilation of dynamic data into geologic models, utilize statistics in the form of covariances calculated using the ensemble of models to perform model updates. These covariance based updates do not preserve the complex spatial characteristics of geologic structures that are better represented using multiple-point statistics. Additionally, in EnKF, the mismatch between the observed and the simulated values are assumed to be linearly related to the parameter updates, and the distribution of the state variable is assumed to be multi-Gaussian. The spatial distribution of the primary variables like facies can be non-Gaussian because of the spatial continuity exhibited by channel facies (sands) and non-channel facies (clay), that have distinctly different properties. Also, the relationship between the mismatch and the parameter updates can be strongly non-linear. In indicator-based data assimilation (InDA) method the indicator transforms of parameter and mismatch variables are used, which affords the treatment of these variables as bi-variate non-Gaussian. Furthermore, in the indicator transformed space, restrictive linear assumption can also be lifted as the indicator transform is invariant under non-linear transformations. However, the updates are still governed by the bivariate interactions between the state variables. A multiple-point extension of InDA is proposed in this paper which utilizes existing multiple-point simulation algorithms like single normal equation simulation (SNESIM) in combination with InDA, to preserve the spatial characteristics of the geologic model while at the same time honor the observed flow or transport response. The proposed method is applied to a synthetic groundwater system with complex channel distributions. The channel features of the final updated ensemble of models is shown to converge towards the reference model and the ensemble flow responses are also shown to match the reference flow response.

Application of GIS-based decision-making model to evaluate safety of underground mining under Neogene aquifers

This paper presents a decision-making model to evaluate the safety of underground coal mining under the Neogene aquifers which are composed of sand based on geographic information system and entropy values. An evaluation index system, which contains the main factors that determine the safety of underground mining under the Neogene aquifers is proposed by considering the geological and hydrogeological conditions. Then, the thematic maps of the factors are used to analyse the complex spatial characteristics of mining safety under the Neogene aquifers. A model is proposed based on a mathematical model of entropy, which is used to describe the complex nonlinear relationships between the index system and mining safety under the Neogene aquifers. As a result, a comprehensive decision for mining under the Neogene aquifers is also shown by thematic mapping. The decision model is demonstrated by using data from the Taiping coalmine in Shandong Province, China. [Received: December 6, 2016; Accepted: August 13, 2017]

‘Only one can rule the night’

This article charts and examines the relationship between popular music and the British travelling fairground in the post-war years. I set out a historical trajectory of the importance of sound and noise within the fairground, and examine how music in the post-war years emerged as an important aspect of this wider soundscape. The complex spatial characteristics of the fairground are considered as fragmented zones with ‘interiors-within-interiors’ forming to facilitate certain music and sounds. This progresses to encompass and engage the subcultural explosion in British society. The fairground becomes a complex mode of musical consumption, enhancing the sound through somatic engagement and providing a temporary space for subcultures to flourish. I draw on historical materials, archival evidence and gathered testimony to look at the 1950s period of new music, then plot a course to the current phase of dance music and club culture.

A Study of Multilevel Banded Graph Cuts for Three-Dimensional Colon Tissue Segmentation

Graph cuts is an image segmentation method by which the region and boundary information of objects can be revolved comprehensively. Because of the complex spatial characteristics of high-dimensional images, time complexity and segmentation accuracy of graph cuts methods for high-dimensional images need to be improved. This paper proposes a new three-dimensional multilevel banded graph cuts model to increase its accuracy and reduce its complexity. Firstly, three-dimensional image is viewed as a high-dimensional space to construct three-dimensional network graphs. A pyramid image sequence is created by Gaussian pyramid downsampling procedure. Then, a new energy function is built according to the spatial characteristics of the three-dimensional image, in which the adjacent points are expressed by using a 26-connected system. At last, the banded graph is constructed on a narrow band around the object/background. The graph cuts method is performed on the banded graph layer by layer to obtain the object region sequentially. In order to verify the proposed method, we have performed an experiment on a set of three-dimensional colon CT images, and compared the results with local region active contour and Chan–Vese model. The experimental results demonstrate that the proposed method can segment colon tissues from three-dimensional abdominal CT images accurately. The segmentation accuracy can be increased to 95.1% and the time complexity is reduced by about 30% of the other two methods.

Nonlinear waves in lattice materials: Adaptively augmented directivity and functionality enhancement by modal mixing

The motive of this work is to understand the complex spatial characteristics of finite-amplitude elastic wave propagation in periodic structures and leverage the unique opportunities offered by nonlinearity to activate complementary functionalities and design adaptive spatial wave manipulators. The underlying assumption is that the magnitude of wave propagation is small with respect to the length scale of the structure under consideration, albeit large enough to elicit the effects of finite deformation. We demonstrate that the interplay of dispersion, nonlinearity and modal complexity involved in the generation and propagation of higher harmonics gives rise to secondary wave packets that feature multiple characteristics, one of which conforms to the dispersion relation of the corresponding linear structure. This provides an opportunity to engineer desired wave characteristics through a geometric and topological design of the unit cell, and results in the ability to activate complementary functionalities, typical of high frequency regimes, while operating at low frequencies of excitation - an effect seldom observed in linear periodic structures. The ability to design adaptive switches is demonstrated here using lattice configurations whose response is characterized by geometric and/or material nonlinearities.

Validation of ASTER Surface Temperature Data withIn SituMeasurements to Evaluate Heat Islands in Complex Urban Areas

This study compared Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) surface temperature data within situmeasurements to validate the use of ASTER data for studying heat islands in urban settings with complex spatial characteristics. Eight sites in Changwon, Korea, were selected for analyses. Surface temperature data were extracted from the thermal infrared (TIR) band of ASTER on four dates during the summer and fall of 2012, and correspondingin situmeasurements of temperature were also collected. Comparisons showed that ASTER derived temperatures were generally 4.27°C lower than temperatures collected byin situmeasurements during the daytime, except on cloudy days. However, ASTER temperatures were higher by 2.23–2.69°C on two dates during the nighttime. Temperature differences between a city park and a paved area were insignificant. Differences between ASTER derived temperatures and onsite measurements are caused by a variety of factors including the application of emissivity values that do not consider the complex spatial characteristics of urban areas. Therefore, to improve the accuracy of surface temperatures extracted from infrared satellite imagery, we propose a revised model whereby temperature data is obtained from ASTER and emissivity values for various land covers are extracted based onin situmeasurements.

Spatial arrangement of cerebro-pontine terminals.

Understanding the interaction of the cerebral cortex and cerebellum requires knowledge of the highly complex spatial characteristics of cerebro-cerebellar signal transfer. Cerebro-pontine fibers from one neocortical site terminate in several sharply demarcated patches across large parts of the pontine nuclei (PN), and fibers from different neocortical areas terminate in the same pontine region. To determine whether projections from segregated neocortical sites overlap in the PN, we studied double anterograde tracing of cerebro-pontine terminals from large parts of rat neocortex. In none of these experiments, including double injection into two functionally related areas, were we able to demonstrate overlapping patches, although close spatial relationships were always detected. This non-overlapping distribution is consistent with a compartmentalized organization of the cerebro-pontine projection and may be the basis of the fractured type of maps found in the cerebellar granular layer. The critical distance between two sites on the neocortical surface that project to non-overlapping patches in the PN was found to be 600 microm, by using double injection within the whisker representation of the primary somatosensory area. This matches the diameter of dendritic trees of layer 5 projection neurons, indicating that non-overlapping populations of neocortical projection neurons possess non-overlapping patches of pontine terminals. Estimations based on this critical distance and the pontine volume anterogradely labeled by one injection site indicate that the size of the PN may be well suited to accommodate a complete set of non-overlapping pontine patches from all possible neocortical sites.