Hierarchical Decomposition Method Research Articles

Design of a sustainable biorefinery for the valorization of waste from the baker’s yeast industry through process simulation: A case study for the production of animal feed, fertilizers, and biofuels from vinasse

The present work deals with the biotechnological valorization of the vinasse through a sustainable biorefinery. The aim was to design and analyze, through process simulation, a biorefinery for the valorization of vinasse in products such as organo-mineral fertilizers, supplements for animal feed, and biogas. For this purpose, the SuperPro Designer software was used, and a modified hierarchical decomposition method was applied. One base case and three scenarios were techno-economically assessed, and the net present value was used as a selection criterion. The technological configuration with the best techno-economic criterion was analyzed socially and environmentally. The results suggest that the best configuration of the biorefinery corresponded to scenario 3, with a net present value of USD$73,364,000, a number of direct employees of 38, and a blue water footprint of 16.16 m3/h. These findings highlight the potential of the design of biorefineries to address the valorization of vinasses in Colombia and worldwide.

How to mine the abnormal information of power transformers: An efficient tool for quantifying the fault characteristics via multi-vibration signals

The new power system puts forward higher requirements for the reliability of power equipment. Mining the intrinsic information of equipment operation from massive data is the key challenge to determining fault early warning. The transformer fault diagnosis through vibration signal analysis has gained increasing prominence. However, the vibration signals produced by power transformers exhibit complex nonlinear and multivariate fluctuations due to the interaction of electromagnetic and mechanical factors. Unfortunately, the traditional fault diagnosis models lack the capability to capture extensive global fault information, resulting in inadequate performance in fault identification. This paper proposes the enhanced hierarchical multivariate multiscale fuzzy entropy (EHMvMFE) as an effective method for quantifying defect information in multivariate transformer signals to overcome the challenge. Firstly, the enhanced hierarchical decomposition method and time series coarse-graining theory are introduced into multivariate fuzzy entropy to develop EHMvMFE, which can comprehensively reflect the feature information of multivariate signals from the full range of frequencies. Then, EHMvMFE is utilized to extract the features via multivariate vibration signals of the transformer. Finally, the extreme learning machine is conducted to achieve effective identification of abnormal conditions. The proposed method is applied to two categories of transformer fault cases, and the diagnostic indicators are at least higher than 99.70 % and 91.82 %, which indicates a strong advantage in comparison with other popular models. The approach could be flexibly extended to the fault diagnosis of other power equipment, which is a potentially effective tool for improving the reliability of power systems.

Analysis of Intelligent Risk Rating of Corporate Internal Control Compliance under Information Convergence Technology

Abstract The alarming problems related to illegal construction in individual enterprises have begun to raise questions about the effectiveness of internal control in these enterprises. In order to better achieve the internal control objectives, a set of scientific and complete rating index systems and evaluation models were constructed. This paper first proposes a fuzzy comprehensive evaluation method for enterprise internal control compliance risks, establishes a factor set, the weight allocation judged by the analytic hierarchy process, and builds the internal control evaluation model framework through the hierarchical decomposition method. Secondly, the core indicators in compliance internal control are determined by the form-filling method, the core indicators are filled into the framework determined by the hierarchical decomposition method, and finally, the weights of each indicator in each level are determined through weighting calculation, and the model is built. Through the specific application of an enterprise, it is found that the effectiveness of internal control has developed from “qualified” to “excellent” in the past five years, and the POOR value has shown a downward trend year by year, from a high of 0.2821 to 0.0717. The only thing that has not developed to excellent is that in 2022, the enterprise did not carry out construction under the provisions of the planning permit and was fined 675,900 yuan, and the grade was downgraded to “good”, but the overall degree of realization of the company’s internal control risk rating is improving year by year.

Multiscale hierarchical decomposition methods for ill-posed problems

The multiscale hierarchical decomposition method (MHDM) was introduced in Tadmor et al (2004 Multiscale Model. Simul. 2 554–79; 2008 Commun. Math. Sci. 6 281–307) as an iterative method for total variation (TV) regularization, with the aim of recovering details at various scales from images corrupted by additive or multiplicative noise. Given its success beyond image restoration, we extend the MHDM iterates in order to solve larger classes of linear ill-posed problems in Banach spaces. Thus, we define the MHDM for more general convex or even nonconvex penalties, and provide convergence results for the data fidelity term. We also propose a flexible version of the method using adaptive convex functionals for regularization, and show an interesting multiscale decomposition of the data. This decomposition result is highlighted for the Bregman iteration method that can be expressed as an adaptive MHDM. Furthermore, we state necessary and sufficient conditions when the MHDM iteration agrees with the variational Tikhonov regularization, which is the case, for instance, for one-dimensional TV denoising. Finally, we investigate several particular instances and perform numerical experiments that point out the robust behavior of the MHDM.

Hierarchical Tensor Decomposition of Module Partition for the Mechanical Simulation

The mechanical simulation experiment can provide guidelines for the structural design of materials, but the module partition of mechanical simulation experiments is still in its infancy. A mechanical simulation contour, e.g., strain and stress contour, has hierarchical characteristics. By analyzing the contour at different layers, the physical properties of the structure material can be improved. Current state-of-the-art methods cannot distinguish between simulation strain contours, as well as sparsely distributed spots of strain (stress concentrations) from simulation strain contour images, resulting in simulation data that does not accurately reflect real strain contours. In this paper, a Hierarchical Tensor Decomposition (HTD) method is proposed to extract hierarchical contours and stress concentrations from the simulation strain contours and then improve the mechanical simulation. HTD decomposes a tensor into three classes of components: the multi-smooth layers, the sparse spots layer, and the noise layer. The number of multismooth layers is determined by the scree plot, which is the difference between the smooth layers and the sparse spots layer. The proposed method is validated by several numerical examples, which demonstrate its effectiveness and efficiency. A further benefit of the module partition is the improvement of the mechanical structural properties.

Hierarchical wave-mode separation in the poroelastic medium using eigenform analysis

In the elastic medium, the scalar and vector P- and S-waves decomposition has been extensively studied and some strategies can be extended to the poroelastic medium to extract P- and S-wavefields. However, there are three propagation modes in the poroelastic medium in Biot’s theory, namely, a fast P wave, a slow P wave, and an S wave. Because the propagation characteristic of a slow P wave is different from that of a fast P wave and S wave, the wavefield separation methods in the elastic medium cannot be directly applied to the poroelastic medium to produce a complete wave-mode separation. Based on the eigenform analysis, we have developed a hierarchical wavefield decomposition method to completely separate S waves and fast and slow P waves in the poroelastic medium. Using the Helmholtz decomposition, we first compute scalar and vector potential wavefields to separate P and S waves. Then, a cross-product operator is proposed to decompose fast and slow P waves based on their different polarization directions. To produce correct amplitudes and phases, we apply another cross-product operator and an amplitude correction term to the separated wavefields. Three numerical examples demonstrate that our method can produce accurate fast P-wave, slow P-wave, and S-wave separation results, and the decomposed fast and slow P waves have the same phases and amplitudes as the P-wave potential wavefields.

Contributions of climatic and social factors to two water use efficiency indicators in the Yellow River

AbstractTracing the spatiotemporal tendency and driving forces of water use efficiency (WUE) is of great significance for understanding the carbon‐water balance, especially in large river basins. The lack of differential comparisons regarding the tendencies and driving forces of WUE indicators between the upper and lower reaches of rivers often affects the collaborative allocation and utilization of water resources at the basin scale, especially social factors such as population and economy distributions. Using remote sensing data and hierarchical decomposition methods, we identified the spatiotemporal distribution of the WUE of ecosystems (WUEe) and the WUE of canopies (WUEc) in the Yellow River Basin from 2003 to 2017 as well as the contribution rates of climatic and social factors to WUEe and WUEc. Our results show that WUEe had a different distribution pattern than WUEc. An increase in WUEe was accompanied by a decrease in WUEc, and these changes varied among the upper, middle and lower reaches. WUEc was most negatively correlated with pressure (| r | = 0.65), while WUEe was most positively correlated with the precipitation rate (| r | = 0.68). The contribution rates of climatic factors to both WUEc and WUEe in the upper reaches were higher than 95%. The middle reaches were mainly driven by climatic factors, with contribution rates higher than 90%. In the lower reaches, the contribution rates of social factors to WUEe were higher than 30%. These differentiated contributions indicated the importance of considering social factors in the lower reaches than in the upper reaches when managing WUEe under the dominance of climatic factors on WUEc.

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger in IoT

In order to maximize the work efficiency of wireless mobile charger, a payoff-maximization-based adaptive hierarchical wireless charging algorithm for mobile charger is proposed. Based on the mesh structure and multi-node charging technology, the recharging optimization for massive devices is modelled as a problem of payoff maximization. According to energy allocation, anchor point deployment and time allocation, we decompose it into three layers by the hierarchical decomposition method to obtain optimal solution quickly. The process of energy allocation and anchor point deployment in each mesh is optimized in the first two layers based on Karush–Kuhn–Trucker (KKT) condition and greedy strategy, respectively. Based on the feedback of the first two layers, the most complex problem of time allocation in the last layer is solved by our innovative gain recall mechanism. The trade-off between the number of recharged devices and recharging time in each cycle can be achieved by only charging the devices in the meshes which are without recall gains. The simulation results prove our algorithm can adaptively adjust the ratio of moving time to recharging time in a fixed cycle, and mobile charger can always work in efficient recharging positions, whose effect is exploited utmost.

Multiscale Hierarchical Image Decomposition and Refinements: Qualitative and Quantitative Results

The multiscale hierarchical decomposition method (MHDM) proposed in [E. Tadmor, S. Nezzar, and L. Vese, Multiscale Model. Simul., 2 (2004), pp. 554--579] has been proven very appropriate for denois...

Wireless Transmission Method for Large Data Based on Hierarchical Compressed Sensing and Sparse Decomposition.

With the widespread application of wireless sensor networks, large-scale systems with high sampling rates are becoming more and more common. The amount of original data generated by the wireless sensor network is very large, and transmitting all the original data back to the host wastes network bandwidth and energy. This paper proposes a wireless transmission method for large data based on hierarchical compressed sensing and sparse decomposition. This method includes a hierarchical signal decomposition method based on the same sparse basis and different sparse basis hierarchical compressed sensing method with a mask. Compared with the traditional compressed sensing method, this method reduces the error of signal reconstruction, reduces the amount of calculation during signal reconstruction, and reduces the occupation of hardware resources. We designed comparison experiments between the traditional compressed sensing algorithm and the method proposed in this article. In addition, the experiments’ results prove that our proposed method reduces the execution time, as well as the reconstruction error, compared with the traditional compressed sensing algorithm, and it can achieve better reconstruction at a relatively low compression ratio.

PolySimp: A Tool for Polygon Simplification Based on the Underlying Scaling Hierarchy

Map generalization is a process of reducing the contents of a map or data to properly show a geographic feature(s) at a smaller extent. Over the past few years, the fractal way of thinking has emerged as a new paradigm for map generalization. A geographic feature can be deemed as a fractal given the perspective of scaling, as its rough, irregular, and unsmooth shape inherently holds a striking scaling hierarchy of far more small elements than large ones. The pattern of far more small things than large ones is a de facto heavy tailed distribution. In this paper, we apply the scaling hierarchy for map generalization to polygonal features. To do this, we firstly revisit the scaling hierarchy of a classic fractal: the Koch Snowflake. We then review previous work that used the Douglas–Peuker algorithm, which identifies characteristic points on a line to derive three types of measures that are long-tailed distributed: the baseline length (d), the perpendicular distance to the baseline (x), and the area formed by x and d (area). More importantly, we extend the usage of the three measures to other most popular cartographical generalization methods; i.e., the bend simplify method, Visvalingam–Whyatt method, and hierarchical decomposition method, each of which decomposes any polygon into a set of bends, triangles, or convex hulls as basic geometric units for simplification. The different levels of details of the polygon can then be derived by recursively selecting the head part of geometric units and omitting the tail part using head/tail breaks, which is a new classification scheme for data with a heavy-tailed distribution. Since there are currently few tools with which to readily conduct the polygon simplification from such a fractal perspective, we have developed PolySimp, a tool that integrates the mentioned four algorithms for polygon simplification based on its underlying scaling hierarchy. The British coastline was selected to demonstrate the tool’s usefulness. The developed tool can be expected to showcase the applicability of fractal way of thinking and contribute to the development of map generalization.

Weighted Linking Decomposition: Mining Denser and More Compact Hierarchies for Bipartite Graphs

Dense subgraph detection is a well-known problem in graph theory. The hierarchical organization of graphs as dense subgraphs, however, goes beyond simple clustering, as it allows the analysis of the network at different scales.Although there are several hierarchical decomposition methods for unipartite graphs, only a few approaches for the bipartite case have been proposed. In this work, we explore the problem of hierarchical decomposition for bipartite graphs.We propose an algorithm called Weighted Linking that identifies denser and more compact hierarchies than the state of the art approach. We also propose a new score to help choose the best between two hierarchical decompositions of the same graph.The proposed algorithm was evaluated experimentally using six real-world datasets and identified smaller and denser hierarchies on most of them.

A Two-Phase Coordinated Planning Approach for Heterogeneous Earth-Observation Resources to Monitor Area Targets

Monitoring various types of disasters involves diversified requirements, such as the spectral band, resolution, and timeliness. However, at present, different types of observation platforms are separately operated. This isolated resource organization model is insufficient to meet the requirements of various Earth-observation tasks, especially when disasters occur. As a result, it is necessary to construct an Earth-observation network that contains space-air-ground observation resources and makes unified task planning for the included heterogeneous resources, such that the efficiency of the entire observation system is maximized. In this article, an architecture with two planning phases is proposed for the coordinated planning of heterogeneous Earth-observation resources, in which area targets and four types of space-air-ground observation resources [i.e., satellites, unmanned aerial vehicles (UAVs), airships, and ground monitoring vehicles] are considered. The two-phase approach in this architecture includes an area target decomposition phase and a task allocation phase. In the first phase, an area target hierarchical decomposition (ATHD) method is proposed to decompose the area targets into subtasks. In the second phase, a task conflict heuristic allocation (TCHA) method is proposed to allocate the decomposed subtasks to subplanning centers. Extensive experiments on simulated and realistic scenarios are conducted to verify the effectiveness of the proposed ATHD and TCHA methods. The computational results show that the ATHD method substantially improves the efficiency of the coordinated task planning process. Moreover, compared with traditional task allocation methods, the TCHA method could produce high-quality observation plans for the Earth-observation network, as it brings complementary benefits via the comprehensive usage of heterogeneous space-air-ground resources.

Optimisation of indoor hybrid PLC/VLC/RF communication systems

In this study, the authors propose a hybrid power line communication (PLC)/visible light communication (VLC)/radio frequency (RF) fronthaul with a fibre-based wired backhaul system to support massive number of smart devices (SDs). Since a signal-to-noise ratio-based access point (AP) association and bandwidth (BW) allocation for each SD do not necessarily improve the system capacity, they propose novel and efficient AP association and BW allocation strategies to maximise the sum rate capacity (SRC) of the hybrid system under consideration. An optimisation problem is formulated for the SRC with the AP association and BW allocation as the optimisation parameters and a hierarchical decomposition method is used to convert the non-linear optimisation problem into a set of convex optimisation problems. Then, the proposed strategies are used to solve the optimisation problem in an iterative manner until the SRC converges to an optimal value. Further, an analytical approximation for the BW allocated to each SD for a given AP association is derived using the Lagrangian multiplier method. The performance of the proposed system is evaluated through extensive numerical results. Moreover, the effect of the increased number of SDs on the optimal SRC is analysed.

Cache Replacement Strategy With Limited Service Capacity in Heterogeneous Networks

Recently, cellular networks face a huge challenge to satisfy large increment of user demands for higher-speed and lower-latency communication service. One promising solution is to apply the cache technology in edge cache networks to reduce redundant data transmission. The cache technology is effective, but the cache capacity is limited. Researchers have proposed various cache strategies to efficiently utilize the limited cache capacity. However, most existing solutions have not taken into account the service capacity limitation of mobile devices. In this paper, we propose a cache replacement strategy for heterogeneous networks considering the limitation of service capacity and user mobility. In the cache replacement strategy, we utilize the user characteristics, such as user mobility and file popularity, to estimate the user demands, and then define the system cost. We formulate the cache strategy design as a mixed integer linear programming problem to minimize the system cost, and use Lagrangian relaxation and hierarchical primal-dual decomposition method to solve this problem. Numerical results show that the proposed cache strategy can significantly reduce the system cost and increase the cache hit ratio compared to the cache strategy that does not consider the limitation of user service capacity.

A domain association hierarchical decomposition optimization method for cab vibration control of commercial vehicles

The dynamical vibration responses of a commercial vehicle’s cab can seriously affect ride comfort; Multi-source excitation effects of commercial vehicle will cause a great interference for specific research target. This paper presents a domain association hierarchical decomposition method, researches the possibility of decreasing vibration responses from related vibration transfer path research, enhances the clarity of the analysis and simplifies the number of decomposed subsystems. Firstly, the whole vehicle model is decomposed into subsystem layers and part layers relevant to a cab, and the cab vibration acceleration value is chosen as the optimization objective. Secondly, in terms of different decomposed layer subsystems, the constraint function and the weight factors of the corresponding mathematical models are established, and the elastic element parameters of the hierarchical fault levels are optimized with the appropriate matching improvement scheme. Thirdly, due to the linear parameter matching scheme of the cab mount not being obvious, a series of improved scaling weight factors and combined linear and nonlinear schemes are introduced based on an experimental design. Finally, by proposing an optimization criterion of maximum deviation degree, the optimal matching scheme is found through the interaction of information between the subsystem layers and part layers, obtaining the most suitable matching mode and damping scaling ratio for the part layer. The experimental results show the effect of proposed method and prove its advantages in countering vibration control problems.

Hierarchical decomposition method and combination forecasting scheme for access load on public map service platforms

The Internet and mobile networks have grown and expanded, and public map services have penetrated into people’s daily lives, thereby generating high-intensity access loads on the platforms that service massive amounts of users. Cloud computing can be used to improve the service performance, but its pricing mode is demand-based as a commercial service. Estimating the scale of user requests in advance is important for meeting the demands of users in a timely manner and allocating appropriate resources in the cloud. In this study, we designed effective methods for describing and predicting the access load on public map service platforms (PMSPs). We propose a novel hierarchical decomposition model for describing the components and structure of access loads, where this model accommodates the non-stationary, variable intensity, and periodic nature of access loads. Furthermore, we propose a wavelet transform–time series decomposition–autoregressive integrated moving average (WT–TSD–ARIMA) prediction scheme to capture and predict data components with various different frequencies and characteristics based on a combination of one-level WT, TSD, and simple linear prediction models (such as ARIMA). Our experimental results show that this prediction scheme significantly improves the prediction performance for linear prediction models while maintaining their stable long-term prediction capacity. The proposed method may facilitate the reliable and adaptive scalable management of cloud computing resources for PMSPs in real time.

Spatial Inequalities in Indonesia, 1996–2010: A Hierarchical Decomposition Analysis

This study analyzes spatial inequalities in Indonesia from 1996–2010 using the hierarchical decomposition method. It uses household expenditures rather than regional accounts and tries to investigate the contributions of spatial inequalities to overall expenditure inequality. We find that urban–rural disparity constitutes 15–25% of overall expenditure inequality. A large difference exists between urban and rural areas in the magnitude of inequality among districts. After controlling for the urban and rural difference, inequality among districts accounts for 15–25% of overall inequality. While disparity between five major island regions is negligible, inequalities between districts within provinces appear to have played an increasingly important role in both urban and rural areas. Given unequal geographic distributions of resource endowments, public infrastructure and economic activities, some spatial inequalities are inevitable. Nevertheless, sustained efforts are necessary to reduce spatial inequalities to facilitate national unity, cohesion and stability.

Optimal Cooperative Content Caching and Delivery Policy for Heterogeneous Cellular Networks

To address the explosively growing demand for mobile data services in the 5th generation (5G) mobile communication system, it is important to develop efficient content caching and distribution techniques, aiming at significantly reducing redundant data transmissions and improving content delivery efficiency. In heterogeneous cellular network (HetNet), which has been deemed as a promising architectural technique for 5G, caching some popular content items at femto base-stations (FBSs) and even at user equipment (UE) can be exploited to alleviate the burden of backhaul and to reduce the costly transmissions from the macro base-stations to UEs. In this paper, we develop the optimal cooperative content caching and delivery policy, for which FBSs and UEs are all engaged in local content caching. We formulate the cooperative content caching problem as an integer-linear programming problem, and use hierarchical primal-dual decomposition method to decouple the problem into two level optimization problems, which are solved by using the subgradient method. Furthermore, we design the optimal content delivery policy, which is formulated as an unbalanced assignment problem and solved by using Hungarian algorithm. Numerical results have shown that the proposed cooperative content caching and delivery policy can significantly improve content delivery performance in comparison with existing caching strategies.