Irregular Network Research Articles

Triangle-based Horizontal Geodetic Datum Transformations in Bosnia and Herzegovina

The article describes the procedure for transformation between old and new horizontal geodetic datum in Bosnia and Herzegovina. Two triangle-based methods were used for transformation, which are based on irregular and regular triangular network. For development of transformation models two set of points were used, one for developing models (around 1200 points), and other for testing (around 850 points). Prior to development, all points were tested at presence of outliers, and outliers are marked in the points database. Results shows that large part of distortions in old triangulation network can be modeled with used methods. Maximal positional standard deviations with best model are 4.5 and 6.4 cm for two sets of points, respectively, while maximal positional discripencies are 30 and 40 cm for two sets of points. Each method has some advantages and disadvantages which are shown in this article. It is shown that the number, spatial distribution and quality of input data are crucial for development of highly accurate transformation model. Also, as an important contribution of this work, some problematic areas with irregular distortions are identified. Finally, some recommendations are given for improvement of developed models.

A General and Effective Method for Wall and Protrusion Separation from Facade Point Clouds

As a critical prerequisite for semantic facade reconstruction, accurately separating wall and protrusion points from facade point clouds is required. The performance of traditional separation methods is severely limited by facade conditions, including wall shapes (e.g., nonplanar walls), wall compositions (e.g., walls composed of multiple noncoplanar point clusters), and protrusion structures (e.g., protrusions without regularity, repetitive, or self-symmetric features). This study proposes a more widely applicable wall and protrusion separation method. The major principle underlying the proposed method is to transform the wall and protrusion separation problem as a ground filtering problem and to separate walls and protrusions using ground filtering methods, since the 2 problems can be solved using the same prior knowledge, that is, protrusions (nonground objects) protrude from walls (ground). After transformation problem, cloth simulation filter was used as an example to separate walls and protrusions in 8 facade point clouds with various characteristics. The proposed method was robust to the facade conditions, with a mean intersection over union of 90.7%, and had substantially higher accuracy compared with the traditional separation methods, including region growing-, random sample consensus-, multipass random sample consensus-based, and hybrid methods, with mean intersection over union values of 69.53%, 49.52%, 63.93%, and 47.07%, respectively. Besides, the proposed method was general, since existing ground filtering methods (including the maximum slope, progressive morphology, and progressive triangular irregular network densification filters) can also perform well.

Dredging Volume Analysis Using Different Software

Dredging is one of the seaport’s regular and necessary maintenance activities to keep the sea depth under control. The purpose of dredging is to provide safe navigation where the depth of the water column must be more profound than the ship’s draft. Periodic bathymetric surveys using an echosounder can observe changes in the topography of the seabed. Topographic changes over time can be used to calculate the volume of sedimentation. Triangular Irregular Network (TIN) is one of the methods to calculate cut and fill volume in some software. This study aims to compare the difference in the sediment volume calculation results from interpolation data with EIVA and CAD. This study uses data from Multibeam Echosounder, carried out before dredging (mc0) and after dredging (mc100). This study shows the difference in volume calculation because of dredging is 693,228.00 m3 using CAD and 701,700.633 m3 using EIVA. Statistic test for volume calculation from this software using a p-value of Friedman test shows no significant difference between these two software calculations.

Application of Fractal to Evaluate the Drying Shrinkage Behavior of Soil Composites from Recycled Waste Clay Brick

Soil drying cracking is the most common natural phenomenon affecting soil stability. Due to the complexity of the geometric shapes of soil cracks during the cracking process, it has become a major problem in engineering science. The extremely irregular and complex crack networks formed in civil engineering materials can be quantitatively investigated using fractal theory. In this paper, fractal dimension is proposed to characterize the drying cracking characteristics of composite soil by adding recycled waste brick micro-powder. At the same time, the concept of the probability entropy of cracking is introduced to quantify the ordered state of crack development. Correspondingly, the endpoint value of probability entropy was solved mathematically, and the meaning of the probability entropy of cracking was clarified. In this study, the fracture fractal characteristics of composite soil mixed with different materials were first investigated. Then, five groups of composite soil-saturated muds with added recycled waste brick micro-powder of different contents were prepared in the laboratory. Using the evaporation test under constant temperature and humidity, the change rules of the fractal dimensions, probability entropy, crack ratios, and water contents of cracks during the cracking process of the soil samples were obtained. The results show that: (1) on the whole, the fractal dimensions of the soil samples added with recycled waste brick micro-powder of different contents increased over time, and the fractal dimensions of the soil samples without recycled waste brick micro-powder were obviously larger than those of the soil samples with recycled waste brick micro-powder. With the increase in the content of recycled waste brick micro-powder, the maximum fractal dimension decreased in turn. The maximum fractal dimensions of the five groups of soil samples were 1.74, 1.68, 1.62, 1.57, and 1.45. (2) The change trends of the probability entropy and fractal dimensions were similar; both of them showed an upward trend over time, and the probability entropy of the soil samples without recycled waste brick micro-powder was greater than that of the soil samples with recycled waste brick micro-powder. With the increase in the contents of recycled waste brick micro-powder, the probability entropy decreased in turn. The maximum values of the crack probability entropy of the five groups of soil samples were 0.99, 0.92, 0.87, 0.83, and 0.80. (3) Under the action of continuous evaporation, the moisture contents of the soil samples gradually decreased over time, while the crack ratios increased over time. To sum up, both from the perspective of the development process of the cracks of the soil samples and from the perspective of the final stable crack networks of the soil samples, the geometric shapes of the cracks of the soil samples without recycled waste brick micro-powder were the most complex. With the increase in the content of recycled waste brick micro-powder, the fractal characteristics of the cracks gradually changed from complex to simple.

Textural characterization of calcium salts-induced mung bean starch-flaxseed protein composite gels as dysphagia food

Effects of calcium gluconate (CG), calcium lactate (CL) and calcium dihydrogen phosphate (CDP) on the structural and functional properties of mung bean starch (MBS)-flaxseed protein (FP) composite gels were investigated to explore the feasibility of developing dysphagia food. The water-immobilizing, rheological and structural properties of MBS-FP composite gels adding different calcium salts (10, 30, and 50 mmol/L) were analyzed by low-field nuclear magnetic resonance measurement, rheological and textural analyses, fourier transform infrared spectroscopy, scanning electron microscopy and confocal laser scanning microscopy. Results showed that calcium salts imparted various soft gel properties to the composite gels by influencing the interactions between MBS and FP. Calcium salts could affect the conformation of amylose chains, accelerate the aggregation of FP molecules, and increase the cross-linking between starch and protein aggregates, resulting in the formation of large aggregates and a weak gel network. Consequently, calcium salts-induced composite gels showed lower viscoelastic moduli and gel strength than the control gel. In particular, different calcium salts had various impacts on the gel properties due to their diverse ability forming hydrogen bonds. Compared with CL and CDP, the gels containing CG presented the higher viscoelastic moduli and hardness, and possessed an irregular cellular network with the increased pore number and the decreased wall thickness. The gel containing 50 mmol/L CL had the highest water-holding capacity, in all the gels tested, by retaining more immobilized and mobile water in the compact gel network with larger cavities. The gels adding CDP presented lower hardness and gumminess due to the obvious lamellar structure within the network. International dysphagia diet standardization initiative (IDDSI) tests indicated that the gels adding CG and CL could be categorized into level 6 (soft and bite-sized) dysphagia diet, while the samples adding CDP could be classified into level 5 (minced and moist). These findings provide insights for the development of the novel soft gel-type dysphagia food.

Improving short-term bike sharing demand forecast through an irregular convolutional neural network

As an important task for the management of bike sharing systems, accurate forecast of travel demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In recent years, many deep learning algorithms have been introduced to improve bicycle usage forecast. A typical practice is to integrate convolutional (CNN) and recurrent neural network (RNN) to capture spatial–temporal dependency in historical travel demand. For typical CNN, the convolution operation is conducted through a kernel that moves across a “matrix-format” city to extract features over spatially adjacent urban areas. This practice assumes that areas close to each other could provide useful information that improves prediction accuracy. However, bicycle usage in neighboring areas might not always be similar, given spatial variations in built environment characteristics and travel behavior that affect cycling activities. Yet, areas that are far apart can be relatively more similar in temporal usage patterns. To utilize the hidden linkage among these distant urban areas, the study proposes an irregular convolutional Long-Short Term Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast. The model modifies traditional CNN with irregular convolutional architecture to leverage the hidden linkage among “semantic neighbors”. The proposed model is evaluated with a set of benchmark models in five study sites, which include one dockless bike sharing system in Singapore, and four station-based systems in Chicago, Washington, D.C., New York, and London. We find that IrConv+LSTM outperforms other benchmark models in the five cities. The model also achieves superior performance in areas with varying levels of bicycle usage and during peak periods. The findings suggest that “thinking beyond spatial neighbors” can further improve short-term travel demand prediction of urban bike sharing systems.

Estimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density

Light detection and ranging (lidar) scanning systems can be used to provide a point cloud with high quality and point density. Gridded digital elevation models (DEMs) interpolated from laser scanning point clouds are widely used due to their convenience, however, DEM uncertainty is rarely provided. This paper proposes an end-to-end workflow to quantify the uncertainty (i.e., standard deviation) of a gridded lidar-derived DEM. A benefit of the proposed approach is that it does not require independent validation data measured by alternative means. The input point cloud requires per point uncertainty which is derived from lidar system observational uncertainty. The propagated uncertainty caused by interpolation is then derived by the general law of propagation of variances (GLOPOV) with simultaneous consideration of both horizontal and vertical point cloud uncertainties. Finally, the interpolated uncertainty is then scaled by point density and a measure of terrain roughness to arrive at the final gridded DEM uncertainty. The proposed approach is tested with two lidar datasets measured in Waikoloa, Hawaii, and Sitka, Alaska. Triangulated irregular network (TIN) interpolation is chosen as the representative gridding approach. The results indicate estimated terrain roughness/point density scale factors ranging between 1 (in flat areas) and 7.6 (in high roughness areas), with a mean value of 2.3 for the Waikoloa dataset and between 1 and 9.2 with a mean value of 1.2 for the Sitka dataset. As a result, the final gridded DEM uncertainties are estimated between 0.059 m and 0.677 m with a mean value of 0.164 m for the Waikoloa dataset and between 0.059 m and 1.723 m with a mean value of 0.097 m for the Sitka dataset.

Zero-power optical convolutional neural network using incoherent light

As a new high-speed intelligent computing method, optical neural network (ONN) has the advantage of realizing lower or even zero power computing. For example, the diffractive neural networks based on passive diffraction layer can work without power consumption. However, in the realization methods of current ONN, coherent light is used to carry input information, while the light in the natural scene is incoherent light, which makes it difficult to apply ONN to the actual optical scene for physical observation. In this paper, we propose an irregular incoherent optical convolutional neural network (I2OCNN). The network only uses the reflection and transmission properties of light to realize the controllable rearrangement of two-dimensional incoherent light field on a series of passive optical devices, so as to realize the cross interconnection of optical neurons, and thus realizes the optical convolution neural network of incoherent light with zero power consumption. Since the architecture is based on intensity modulation and incoherent superposition, incoherent light can be used to carry input signals, which solves the problem of practical application of ONN. In addition, the network can achieve irregular convolution. MNIST and Fashion MNIST were used to verify the image recognition capability of 7-layer I2OCNN, and the testing accuracy was 86.95% and 75.68%, respectively. Theoretical reasoning and simulation results show that this architecture can complete the basic image recognition tasks under the condition of incoherence and zero power consumption.

Design and Experimental Study on an Innovative UAV-LiDAR Topographic Mapping System for Precision Land Levelling

Topographic maps provide detailed information on variations in ground elevation, which is essential for precision farmland levelling. This paper reports the development and experimental study on an innovative approach of generating topographic maps at farmland-level with the advantages of high efficiency and simplicity of implementation. The experiment uses a low-altitude Unmanned Aerial Vehicle (UAV) as a platform and integrates Light Detection and Ranging (LiDAR) distance measurements with Post-Processing Kinematic Global Positioning System (PPK-GNSS) coordinates. A topographic mapping experiment was conducted over two fields in Henan Province, China, and primitive errors of the topographic surveying data were evaluated. The Root Mean Square Error (RMSE) between elevation data of the UAV-LiDAR topographic mapping system and ground truth data was calculated as 4.1 cm and 3.6 cm for Field 1 and Field 2, respectively, which proved the feasibility and high accuracy of the topographic mapping system. Furthermore, the accuracies of topographic maps generated using different geo-spatial interpolation models were also evaluated. The results showed that a TIN (Triangulated Irregular Network) interpolation model expressed the best performances for both Field 1 with sparse topographic surveying points, and Field 2 with relatively dense topographic surveying points, when compared with other interpolation models. Moreover, we concluded that as the spatial resolution of topographic surveying points is intensified from 5 m × 0.5 m to 2.5 m × 0.5 m, the accuracy of the topographic map based on the TIN model improves drastically from 7.7 cm to 4.6 cm. Cut-fill analysis was also implemented based on the topographic maps of the TIN interpolation model. The result indicated that the UAV-LiDAR topographic mapping system could be successfully used to generate topographic maps with high accuracy, which could provide instructive information for precision farmland levelling.

Estimation of reservoir sedimentation using bathymetry survey at Shumburit earth dam, East Gojjam zone Amhara region, Ethiopia

Sediment accumulation is a major factor in reducing the useful life of irrigation reservoirs. As a result, information on any changes in storage capacity is required to reduce the reservoir's risk. Therefore, the goal of this research was to identify the amount of sediment accumulated in the reservoir using a bathymetric survey. The bathymetric survey method involves subtracting the two-period capacities (before dam construction and after dam construction). The bathymetric survey of the reservoir was carried out by analyzing volume and surface area using ArcGIS 10.8, and developing the Triangulated Irregular Network (TIN) surface from topo-sheet data. The bathymetric survey showed that 297,000 m3 (362,340 tons) of sediment had accumulated in the reservoir over six years of operation. This means the reservoir's total capacity has been reduced by 7.52% over the last six years. The average rate of sedimentation was assessed to be 49,500 m3/y (60,390 tons/year) and the annual capacity reduction of the reservoir was 1.25 percent. The proportion of dead storage capacity to sediment deposition level was used to calculate the reservoir's useful life. Hence; the sedimentation rates of the dead storage and live storage regions were 31,666.67 m3/year and 48,333.33 m3/year, respectively. Finally, this indicates that the reservoir will not be functional for more than 15 years if the sediment deposition rate remains the same as the previous six years throughout the operation periods.

Teachers’ perceived usefulness, ease of use, and challenges with e-learning utilization sustainability after COVID-19 pandemic in Oyo state, Nigeria

This study examined teachers’ perceived usefulness, ease of use, and challenges with e-learning utilization sustainability after COVID-19pandemic. The research was designed as a descriptive survey research and purposive sampling technique was used to select 30 schoolsand 300 teachers from 630 secondary schools in Oyo State in Nigeria. A questionnaire was developed tagged as teachers’ perceptionson sustainable utilization of e-learning after COVID-19 pandemic with a 0.82 reliability index obtained. The data collected were analyzedusing simple percentage, mean scores, and standard deviation. The results revealed that teachers’ perceived usefulness and ease of use ofe-learning were positive and high. In addition, the study has reported that some factors like irregular network service, no direct trainingfor teachers, and many others were identified as challenges to the use of e-learning for instructional delivery. It was recommended amongothers that policymakers should infuse e-learning instructional strategy into the curriculum for teaching/learning process.

Modeling Wildfire Spread with an Irregular Graph Network

The wildfire prediction model is crucial for accurate rescue and rapid evacuation. Existing models mainly adopt regular grids or fire perimeters to describe the wildfire landscape. However, these models have difficulty in explicitly demonstrating the local spread details, especially in a complex landscape. In this paper, we propose a wildfire spread model with an irregular graph network (IGN). This model implemented an IGN generation algorithm to characterize the wildland landscape with a variable scale, adaptively encoding complex regions with dense nodes and simple regions with sparse nodes. Then, a deep learning-based spread model is designed to calculate the spread duration of each graph edge under variable environmental conditions. Comparative experiments between the IGN model and widely used fire simulation models were conducted on a real wildfire in Getty, California, USA. The results show that the IGN model can accurately and explicitly describe the spatiotemporal characteristics of the wildfire spread in a novel graph form while maintaining competitive simulation refinement and computational efficiency (Jaccard: 0.587, SM: 0.740, OA: 0.800).

Road Network Planning in Tropical Forests Using GIS

This study intended to develop a road network planning for timber harvesting in tropical forests in Peru using georeferenced and field data and Geographic Information System (GIS). The Digital Elevation Model (DEM) Alos Palsar 12.5 m was used. The DEM was processed to generate the hydrography and terrain slope maps. A weighted raster was created using overlapping weights of the slope raster and the hydrography map. We created a least cost path raster by using the weighted raster origin and destination points. We used a Triangular Irregular Network (TIN) to validate the Least Cost Path. In addition, histograms of the trajectory of each path with altitude and slope values ​​ were generated. We observed that the forest road planning using GIS provided better definition (economically and environmentally) of road networks in our forest site than those traditionally defined using conventional mapping techniques.

Sensor Placement for Leak Localization in Water Distribution Networks Based on Graph Convolutional Network

Nowadays, the research on optimal sensor placement (OSP) is very active in the field of leak detection and localization in the water distribution network (WDN). Considering that most of the WDNs are irregular networks, this article proposes a new method for OSP applied to irregular network system conditions. From the initial set of a limited number of sensors placed by node data features, the proposed method makes a completely new change to the limited number of sensor layouts. The solution is based on a graph convolutional network (GCN) algorithm, which combines node features and graph structure features. In order to verify the effectiveness of the algorithm, the nodes selected by the algorithm in this article are used to locate the leakage of the pipeline network. By simulating real leakage scenarios, the method is applied to a small WDN (Anytown) and a large WDN (Net3). The results show that the method proposed in this article outperforms other comparison algorithms to a certain extent.

In-situ observation on filiform corrosion propagation and its dependence on Zr distribution in Mg alloy WE43

The direct industrial importance of the corrosion resistance in WE43 is best emphasized by its extensive use in the automotive, aerospace and electronic industries where weight reduction is a necessary requirement. In this work, the corrosion especially the filiform corrosion in a 3.5 wt.% NaCl solution and their dependence on the Zr distribution for WE43 were studied by weight loss tests, hydrogen evolution tests, electrochemical measurements and microscopic analyses. The Zr distribution significantly influenced the initiation and propagation of the filiform corrosion, and accordingly significantly influenced the corrosion rate. WE43 with a cluster Zr distribution displayed filiform corrosion throughout the entire surface with an irregular network distribution of filaments. WE43 with a uniform line Zr distribution exhibited only a few examples of linear filiform corrosion. WE43 with a dispersive Zr distribution exhibited no filiform corrosion. The stability of the corrosion film was responsible for the filiform corrosion. Anodic polarisation promoted the initiation and progression of the filiform corrosion, while cathodic polarisation had an inhibiting effect on the filiform corrosion. The serrated interface of the filiform corrosion increased the contact area between the substrate and the corrosive medium, and hence increased the corrosion rate.

Optimization of Bus Route Planning in Urban Commuter Networks

Bus routing is one of the most important elements of public transit system planning. This article presents a model for optimizing service headway and a bus route serving an area with a commuter (many-to-one) travel pattern. The bus route is optimized by minimizing the total system cost, including operator and user costs, while considering diagonal links in the study network. A method is developed for transforming this network into a pure grid, which enables construction of pure grid network models applicable to irregular grid networks. A case is presented to demonstrate the application of the model. Results show that the optimal bus route is sensitive to demand distribution over the service area. The developed model is particularly useful for planning a new bus service and evaluating an existing one in many cities embedded with general grid networks.

A one dimensional ubiquitiform damage model for quasi-brittle materials

Considering the self-similar characteristic of irregular pore network of materials during the damage evolution process, a pore ubiquitiform model (PUM) is developed to characterize the multiscale pore or microcrack network of quasi-brittle materials, and then based on the PUM, the ubiquitiform damage model (UDM) is proposed to describe the damage evolution process of quasi-brittle materials under quasi-static uniaxial tensile loading. The values of porosity and pore specific surface area estimated from the PUM and the stress-deformation curves including the softening curve calculated from the UDM are in good agreement with previous experimental data, respectively. It can be found that, the PUM can be adopted to characterize the self-similar multiscale pore network and estimate the porosity and pore specific surface area, and the UDM can describe the damage evolution process of quasi-brittle materials under quasi-static uniaxial tensile loading. Meanwhile, the UDM can characterize the self-similar characteristic of pore network during the damage evolution process of quasi-brittle materials through the evolving ubiquitiform complexity.

Ground point extraction using self-adaptive-grid and point to surface comparison

Extracting accurate ground points by using laser point clouds at the complex terrain of the mining area and the dense vegetation environment is a challenging problem. To address this problem, this paper proposes a combined filtering method based on the Self-Adaptive-Grid method (SAG) and Point to Surface Comparison (PSC). First, the adaptive grid is used to extract initial ground seed points, and then, based on the seed points, a Triangulated Irregular Network (TIN) is constructed, and the distance from the reference surface and TIN is computed. These steps are repeated, In each loop, the non-ground points that are significantly far away from the reference surface will be culled, until the ground point is close to the real terrain, where the initial grid size and the distance from the point to the reference surface are gradually reduced according to the gradient. The proposed algorithm overcomes the difficulty of extracting bare ground due to terrain fluctuations. The ground points are effectively separated from the non-ground points in the laser lidar scan data, and the extracted high-precision ground points can be used as important basic data in the terrain modeling of the mining area, surface subsidence monitoring, and research on the laws of surface subsidence, which has great value in popularization and application.

Preparation of novel ZIF-8 aerogel adsorbent based on cellulose and the application of Cu (II) removal from wastewater

A novel adsorbent (ZIF-8 cattail cellulose aerogel, ZCCA) with excellent adsorption performance was prepared using cellulose as a dispersant of ZIF-8 and applied to Cu(II) removal from wastewater. ZCCA showed an irregular network structure, which was uniformly filled with ZIF-8 particles. Its adsorption mechanism was the electrostatic attraction and chemical coordination of Cu(II). This adsorption process followed the Pseudo-second-order kinetics and the Langmuir isotherm model. The maximum adsorption capacity was 270.2 mg⋅g−1 at 303 K. In addition, the removal efficiency remained 83 % after 5 adsorption–desorption cycles. ZCCA showed outstanding adsorption performance and high potential in practical application.

Evolution of eNaira for Re-engineering the Nigerian Emerging Economy

The Central Bank of Nigeria has recently initiated idea to digitalize the Nigerian currency (Naira) making it possible to be stored in a wallet. The launch of the eNaira has been seen as an avenue to change the narrative of doing business globally without encumbrances. Based on this, the study examined the evolution of eNaira for re-engineering the Nigerian emerging economy. The study specifically sought how eNaira re-engineer’s financial inclusion, direct and transparent welfare intervention, and revenue base in the Nigerian emerging economy. The study adopted a descriptive survey research design with a census of 36 as the sample size. The statistical tool employed for the analysis was the ANOVA. The mean and standard deviation were applied to answer three research questions that guided the study. A probability value of 0.05 significant level was used to test three stated hypotheses. The results revealed that eNaira positively and significantly re-engineer’s financial inclusion and the revenue base in the Nigerian emerging economy. However, it did not support direct and transparent welfare intervention for the citizens. The study further observed that irregular internet network, shortage of electronic devices, increase in financial cost, and activities of hackers may likely hinder this lofty idea. Based on this, the study recommended that the Central Bank of Nigeria should provide a sound financial platform and user-friendly infrastructures that will capture data of all citizens and those operating businesses in/with Nigeria. Finally, the Nigerian Communication Commission and the network providers should endeavor to provide uninterrupted internet networks to consumers.