Solution Path Research Articles

Exploring the Effective Path of Combining Rural Revitalization and Ecological Civilization Construction: Fuzhou City, Fujian Province as an Example

The opinions of the State Council of the Central Committee of the Communist Party of China on the implementation of the strategy for the revitalization of the countryside clearly proposed that by 2050 the countryside will be fully revitalized with strong agriculture, beautiful countryside, and rich in farmers. In recent years, with the promotion of the rural revitalization strategy, the majority of rural areas have ushered in many developments and opportunities, however, they have also suffered serious ecological damage. By taking Fuzhou City, Fujian Province, as an example, this paper discusses the dilemma and solution paths of rural ecological civilization construction in Fuzhou City in the context of rural revitalization, with the intention of realizing the organic integration of rural revitalization and ecological civilization construction, further providing experience and inspiration for the villages in other regions of China.

Overcoming the curse of dimensionality in the numerical approximation of backward stochastic differential equations

Abstract Backward stochastic differential equations (BSDEs) belong nowadays to the most frequently studied equations in stochastic analysis and computational stochastics. BSDEs in applications are often nonlinear and high-dimensional. In nearly all cases such nonlinear high-dimensional BSDEs cannot be solved explicitly and it has been and still is a very active topic of research to design and analyze numerical approximation methods to approximatively solve nonlinear high-dimensional BSDEs. Although there are a large number of research articles in the scientific literature which analyze numerical approximation methods for nonlinear BSDEs, until today there has been no numerical approximation method in the scientific literature which has been proven to overcome the curse of dimensionality in the numerical approximation of nonlinear BSDEs in the sense that the number of computational operations of the numerical approximation method to approximatively compute one sample path of the BSDE solution grows at most polynomially in both the reciprocal 1 / ε $ 1 / \varepsilon $ of the prescribed approximation accuracy ε ∈ ( 0 , ∞ ) $ \varepsilon \in(0, \infty) $ and the dimension d ∈ N = { 1 , 2 , 3 , … } $ d\in {\mathbb{N}}=\{1,2,3,\ldots\} $ of the BSDE. It is the key contribution of this article to overcome this obstacle by introducing a new Monte Carlo-type numerical approximation method for high-dimensional BSDEs and by proving that this Monte Carlo-type numerical approximation method does indeed overcome the curse of dimensionality in the approximative computation of solution paths of BSDEs.

A Business Management Resource-Scheduling Method based on Deep Learning Algorithm

As China’s economic level and industrial volume continue to develop and expand, the traditional methods of business management and resource scheduling demonstrate significant limitations and misalignments with China’s daily economic activities. Traditional industrial and commercial management resource scheduling is highly reliant on manual labor. When confronted with the requirements of large-scale project management, the pure manual mode cannot track the real-time progress of the project flexibly, effectively, and in a timely manner, resulting in the inability to complete the corresponding resource-scheduling work efficiently, which will affect the overall operation progress of the project. Deep neural networks have a high-application potential for solving extremely complex and highly nonlinear optimization problems. Artificial intelligence and deep learning research have advanced at a rapid pace over the past few decades, thanks to the efforts of numerous researchers. Combined with GPU technology, the deep learning framework can provide an extremely complex optimization problem with a practical and feasible optimization scheme and corresponding solution path in a very short amount of time. Therefore, this paper explores the potential application of deep learning technology to industrial and commercial resource-scheduling management. By analyzing the benefits of deep learning technology and the bottleneck issues of existing industrial and commercial resource scheduling, a real-time optimized industrial and commercial resource-scheduling model based on deep learning technology is developed. The model is evaluated using the respective data set. The test results demonstrate that the resource-scheduling model proposed in this paper has strong real-time and high-operation efficiency and can assist engineers in completing the corresponding resource-scheduling tasks.

Intelligent Carom Billiards Assistive System for automatic solution path generation and actual path prediction with principal component analysis-based ball motion detection

This article proposes an Intelligent Carom Billiards Assistive System for a novice at Carom Billiards. Since a novice player is very difficult to find the solution paths of the cue ball, the ICBAS automatically calculates the solution paths for given ball configuration which is detected by a monocular vision and displays them in a guidance monitor. For rapid and systematically calculating the solution paths, the five and half system that is the most representative method in the field of billiard is implemented. In addition, the paths of the cue ball changed by the direction of the cue stick are predicted in real time and projected onto the pool table bed. By comparing the solution paths of the cue ball with the projected paths, a player can hit the cue ball easily and successfully. For automatically providing the solution paths and the predicted paths during billiard game, the ball movement is detected by the principal component analysis under linear motion constraints robustly against image noise, occlusion, and so on. Whenever all of the balls stop, both paths are newly calculated and provided for a player. For verifying the feasibility of the proposed Intelligent Carom Billiards Assistive System, the experiments are conducted with the real pool table.

Multi-Point Path Planning Algorithm for a Mobile Robot With Composite Moving Costs

Abstract Multi-point path planning problem is a classic problem of the mobile robot. The present research is concerned with solving the shortest path. In some realistic applications, the shortest path length is not always the significant concerned value of path planning. This article proposes an extended generalized cost concept to constitute the updated path planning strategy. The generalized costs are the combination of straightly moving and turning costs. A genetic algorithm is used to solve optimal path-planning problems. The different weight between the two kinds of costs and how the different parameters affect the optimal path solution is discussed. The generalized cost concept extends the application of path planning to diversified physical quantities. When estimating the composite optimal costs of the path planning problem, we only need to solve the path planning problem with simplex straightly moving costs or simplex turning costs.

Complete Computational Solution of Multi-Class Fluid Queueing Communication Network Models with Piecewise Constant Rate Inputs

This paper develops an explicit characterization and computation of the queueing processes associated with finite buffered multiclass fluid queues and their tandem networks under strict priority and with PieceWise Constant Rate (PWCR) inputs which arise in the performance analysis of communication networks. Analytical and exact computational solutions are developed for accepted arrival, fluid level and departure processes which allow solutions for all other queueing processes. A decomposition of the multiclass queue into a hierarchical sequence of single class queues, termed Virtual Queues, is established and the solutions of each class of the original queue are computed from those of the Virtual Queues taking into account the loosely coupled dynamics of these Virtual Queues. A uniform time sampling sequence, dynamically constructed from the input rate change instances and the boundary hitting times, is employed to develop the iterative transient sample path solutions of all the queueing processes. The processes of a multiclass queue with PWCR inputs are shown to be PWCR processes. The computational solution is extended to tandem networks of multiclass queues in which a class can have different static priorities along its path. The developed algorithm is applied to solve a tandem communication network with three-state Markov modulated voice source inputs. A parametric study of the voice network in terms of long-term time-averaged behaviour of its queueing processes is carried out.

Multi-objective optimization for solving mobile robot path planning problem employing hybridization of algorithms

Mobile Robot Path Planning problem (MRPPP) is the most prominent research area employed in different real-time environments. In this article, a hybrid strategy is employed and validated with different environments for the path planning of mobile robots. The Artificial Potential Field (APF) algorithm and Multi-objective Genetic Algorithm (MOGA) are hybridized to solve an MRPPP. In the proposed hybrid methodology, the implementation is accomplished in three phases. To generate the initial population, an Artificial Potential Field (APF) algorithm is applied to discover all viable routes in an environment between the start and destination points. The collision-free paths are generated by determining the artificial force produced by obstacles and the target. The population-based evolutionary algorithm is employed to derive an optimal solution path from the initial population consisting of candidate paths. The selection operator is used to select the qualified subpopulation to the next generation to converge into the optimal solution. The genetic operators such as two-point crossover and mid-point mutation are exercised which are specific to the path planning problem.

AR-based automatic pipeline planning coordination for on-site mechanical, electrical and plumbing system conflict resolution

Mechanical, Electrical and Plumbing (MEP) system conflicts are complicated problems given the involved multidisciplinary nature and constraint-bearing coordination. Although Building Information Modeling (BIM) shows benefit to resolving the conflicts before construction, the on-site pipeline inspection and further adjustment are inevitably necessary due to practical issues caused by deviations with plans. Given this, the research aims at developing an on-site pipeline inspection and automatic coordination approach by using Augmented Reality (AR) and a grid-based path-planning algorithm. The approach allows the site practitioners to compare the newly added pipe layout plan with the existing pipelines and re-plan the pipe layout where encountered conflicts, further obtaining a solution without clashes. The method considers MEP system constraints in practice and searches conflict-free pipe layout plans in near real-time. Furthermore, the path coordination process also considers MEP design criteria, including its function, construction, and maintenance. The planned pipe layout is displayed through AR to link the information from the virtual model to the real world. The performance tests are conducted in virtual scenarios with different grid cell sizes and priorities of pipes to evaluate the planning results. The test results show that under room and aisle space scenarios, the implemented approach can obtain path solutions with approximately 10 s, and most of them comply with the pre-defined MEP design requirements. Through this study, automatic pipe planning with constraints is proved to be feasible to provide an effective on-site conflict resolution approach to improve the MEP construction process and future maintenance.

How can new energy vehicles become qualified relays from the perspective of carbon neutralization? Literature review and research prospect based on the CiteSpace knowledge map.

Analyzing the feasibility of fuel vehicle transition will be conducive to the realization of the carbon neutralization goal. However, at present, there are few studies specifically aimed at the transition of fuel vehicles. Therefore, this study first analyzed the necessity for the transition of fuel vehicles and then used CiteSpace to analyze 2081 articles in the core Web of Science database in the past decade with "fuel vehicle emission reduction" as the search keyword. After clarifying the research context and development frontier of fuel vehicle emission reduction, we found that most of the literature with the research theme on this topic ends with the research of electric vehicles. Therefore, we took new energy vehicles represented by electric vehicles as the starting point to explore the realization path of carbon neutralization by analyzing the development dilemma and residents' feedback on electric vehicles. Finally, the research review and research prospects were carried out. The study found that although the development of new energy vehicles has made obvious progress at this stage, there are still some problems in comprehensively promoting electric vehicles, such as battery power, charging facilities, and the weak willingness of consumers to accept electric vehicles. Therefore, improving the usage efficiency of new energy vehicles can more effectively force fuel vehicles and new energy vehicles to complete the relay from the perspective of market attraction. This study will provide a more scientific solution and implementation path for the transition of fuel vehicles in various countries.

Feature-guided regularization parameter selection in sparse de-noising for fault diagnosis

Using the sparse and redundant representation model to de-noise vibration signals is an effective method for mechanical fault diagnosis. The selection of the regularization parameter in the sparse de-noising algorithm plays a vital role in reconstructing fault features. The current choices, however, are primarily based on fixed-form parameters derived under the assumption of additive white noise or based on the “trial and error” method. As a result, the extracted de-noised signals often fail to show fault features. To solve this difficulty, a self-adaptive regularization parameter selection strategy is proposed, which directly measures the significance of fault features in sparse de-noised signals. Firstly, the entire solution path is obtained by the computationally efficient least angle regression (LARS) algorithm. Besides, a feature presence probability index (FP) is well designed to measure the significance of fault features in the reconstructed signal envelope demodulation spectrum. Accordingly, with the help of the maximal FP indicator, the optimal regularization parameter and LARS solution is determined. The proposed method is not limited by noise probability distributions; therefore, it shows the superiority of extracting fault features in both the simulation and experimental data analysis.

To promote the kidney transplantation of sensitized recipients in China by multilateral cooperation

Sensitized recipients have an increased risk of kidney transplantation due to the immune memory of human leucocyte antigen. They lag on the waiting list and have poorer outcomes of transplantation. The society should give every patient an equal opportunity for treatment. Facing this worldwide problem, Chinese transplant community has some practical difficulties under current status of transplantation development in our country. It requires the cooperation of transplant laboratories, organ procurement organizations, clinicians, and the national organ allocation system to explore a path of solution.

Creating complex mazes quickly: Solution path first

A maze is a structure of passageways interconnected in a complex way, so that it presents at least one path to reach the designated end starting from a predefined location. A new approach is proposed in this algorithm. First of all a solution path is carved on the opposite edges of the board in order to meet maze definition. Later, walls are built up on the even lines (or odd lines) and some parts of these lines randomly break off. Finally passages are constructed randomly between broken even lines. This technique opens new era to produce different versions of it for maze construction. The algorithm is applied to a 100 × 100 board and evaluation compared with the well-known maze algorithms. Its prominent features are being more speedy (3.5 second) to construct and more complicated (10.32% dead ends) to solve.

Using intra‐task flexibility on an intelligent tutoring system to promote arithmetic problem‐solving proficiency

AbstractProblem solving is often regarded as one of the most essential cognitive functions in our daily lives, and, for that reason, educational theorists have long stressed the need for its development. As cognitive flexibility is a fundamental characteristic necessary throughout the problem‐solving process, the purpose of this study is to analyse students' problem‐solving performance after following intra‐task flexibility‐based training mediated by an intelligent tutoring system. With this aim, 110 fifth and sixth grade students took part in a quasi‐experimental study that included six forty‐five‐minute flexibility‐based training sessions preceded and followed by test sessions in order to evaluate eventual problem‐solving proficiency improvements. The findings show that intra‐task flexibility training enhances students' ability to solve arithmetic word problems, especially when flexibility‐related activities are completed sequentially in the same session and not in different sessions. Furthermore, the proposed instruction is particularly helpful in improving girls' problem‐solving competence, which can minimize eventual gender gaps and strengthen their STEM vocation. Practitioner notesWhat is already known about this topic Cognitive flexibility is beneficial in finding appropriate problem‐solving strategies. Exposing students to a variety of problem‐solving methods or strategies can improve their cognitive flexibility. Boys are more likely than girls to flexibly apply multiple solution paths while solving problems, which leads to gender differences in mathematics. By contrast, girls are more prone to take advantage of instructional advice when using intelligent tutoring systems (ITS). What this paper adds Prompting students to apply multiple resolution strategies within the same problem (intra‐task flexibility instruction) is comparatively more efficient than standard instruction in promoting problem‐solving performance. It is more effective in terms of problem‐solving proficiency to ask students to find different resolutions for a problem sequentially in the same session than to do so in separate sessions. Effectiveness of ITS‐mediated flexibility‐based training leads to the promotion of problem‐solving proficiency, particularly in the case of female students. Implications for practice and/or policy The use of the ITS HINTS has the potential to promote the use of multiple solutions in problem solving by monitoring the resolution paths of the student, which can be difficult in a traditional classroom setting. ITS‐mediated flexibility‐based training is especially effective in improving girls' problem‐solving competence, which can minimize eventual gender gaps and strengthen their STEM vocation.

Dynamic Event-Triggered Impulsive Control for Stochastic Nonlinear Systems With Extension in Complex Networks

This study proposes a novel dynamic event-triggered impulsive control (ETIC) scheme to study the exponential stabilization of general stochastic nonlinear systems, where the impulsive sequence is determined by a dynamic event-triggered mechanism. The dynamic ETIC can effectively reduce controller updates and significantly save energy under the same decay rate compared to traditional static event-triggered impulsive generators. Additionally, there is a guaranteed positive minimum inter-event time for each sample path solution of systems. Furthermore, the proposed dynamic ETIC scheme is employed to stabilize stochastic complex networks based on the graph theory and the Lyapunov method. Finally, we provide two illustrative examples to verify the effectiveness and correctness of the proposed dynamic ETIC scheme.

Energy self-sufficiency and greenhouse gas emission reductions in Latin American dairy farms through massive implementation of biogas-based solutions

The transition towards sustainable economies with improved resource efficiency is today’s challenge for all productive sectors. The dairy sector in Latin America is growing without considering a clear path for sustainable energy and waste management solutions. This study proposes integrated solutions through a waste-to-energy approach. The solutions consider biogas production (via cow manure) as the main energy conversion pathway; technology solutions include biodigesters, power generators, and combined heat and power systems that supply not only the energy services demanded by dairy farms (for cooking gas, electricity, refrigeration and hot water) but also provide organic fertilizers. Biogas’ potential was estimated to verify whether it can cover the energy demands of the farms, while the levelized costs of producing biogas and electricity were the indicators for the techno-economic evaluation of the solutions. Greenhouse gas emission reductions were estimated by following IPCC guidelines. Specifically, the proposed solutions lead to energy self-sufficiency in most dairy farms with relevant biogas and electricity costs in the range of 1.7–3.7 and 6–12 USD cents/kWh, respectively. In addition, implementing the proposed solutions in Latin American dairy farms would allow annual greenhouse gas emission reductions of 32.8 Mton CO2 eq. with an additional 17 Mton if widespread use of the supplied organic fertilizers is achieved.

GEE-Assisted Forward Regression for Spatial Latent Variable Models

Multivariate spatial data, where multiple responses are recorded at a set of spatial locations, are widely collected in many disciplines. One common approach for analyzing such data is spatial generalized linear latent variable models (spatial GLLVMs), where the latent variables are used to model both the spatial correlation between locations and correlations between responses. However, inference such as variable selection for spatial GLLVMs is computationally demanding, as the marginal likelihood involves a high-dimensional and often intractable integral. To overcome this, we propose to use spatial generalized estimating equations (GEEs) to perform fast, GEE-assisted forward regression for spatial GLLVMs. Focusing on counts and nonnegative continuous responses, we use spatial GEEs to build a forward solution path by choosing the candidate variable which maximizes a score statistic at each point on the path. A model is then selected from this path based on a modified score information criterion. The proposed approach is computationally efficient, relying only on GEEs which are quick to update, coupled with a novel theoretical result linking the coefficients from spatial GEEs to that of spatial GLLVMs. We show that the proposed approach can asymptotically identify all truly important nonzero predictors in the underlying spatial GLLVM. Simulations demonstrate that, when the data are generated from a sparse spatial GLLVM, GEE-assisted forward regression performs well at recovering this sparsity, while taking only a fraction of the computation time required to fit just a single (saturated) spatial GLLVM. Supplementary materials for this article are available online.

Sensor-based modeling of problem-solving in virtual reality manufacturing systems

Problem-solving is the process to achieve a goal when the solution path is uncertain. Recently, technological advancements have changed problems’ characteristics and their solutions in engineering fields. Strong problem-solving skills are essential to allow engineers to assess new problems and quickly implement solutions. Engineering problem-solving skills are first educated in schools and usually evaluated through written exams. However, high grades in exams do not represent sufficient problem-solving skills in real-world engineering problems. Decision making with insufficient problem-solving skills in real world may result in costly consequences. Therefore, it is imperative to evaluate and reinforce problem-solving skills of engineering students in real-world problems. With the rapid technological advancements, availability of virtual reality (VR) and eye-tracking facilitates the study of engineering problem-solving. The immersive environment created by VR enables students to better understand and solve real-world engineering problems. On the other hand, eye-tracking allows for studying fundamental cognitive processes during information processing. It is critical to integrate VR simulation with data-driven modeling of eye movements to evaluate and enhance engineering problem-solving skills. In this paper, we integrate sensing technology (i.e., eye-tracking) and virtual reality (VR) to model problem-solving in manufacturing systems. A novel data-driven model that integrates signal detection theory (SDT) with Conflict & Error (C&E) is developed to quantify engineering problem-solving skills. First, we simulate a manufacturing system in a VR game environment. Students are given an assembly problem to produce a car toy that satisfies some particular requirements in the VR manufacturing system while eye-tracking data are collected throughout the assembly process. Second, eye-tracking data are analyzed with a SDT model to quantify problem-solving skills. Third, a joint SDT-C&E model is developed to analyze eye-tracking data and benchmark with results generated from the SDT model. Experimental results show that the joint SDT-C&E model is more effective to quantify problem-solving skills of engineering students than the SDT model.

A Multi-Path Based Embedding Scheme at Perfect Maze

Steganography is a process of hiding data in a media, e.g., image, audio, video, etc. There are lots of areas where steganography can be used. Many researchers are devoting their valuable efforts to hide messages before sending them to a destination through maze-based steganography. However, maze-based steganography has the drawback of reducing embedding capacity. Another problem of maze application at embedding is the imperfection of its solution paths. Imperfect paths are not used for embedding by the maze. That is why while selecting multiple paths, the process of Niwayama et al. in 2010 prunes many paths considering their overlapping or crossing affairs with other ones. In this paper, a solution to those stated problems of Niwayama et al. is outlined and successful results found are demonstrated here too. First, an imperfect maze is tried to be changed virtually to a perfect maze. Then solution paths from start to end are generated. While generating solution paths, the longest path among them is considered to cross or join a point along their pathways. Therefore, one path among those imperfect paths is considered embedded at and extraction was not employed by any earlier proposal, so far we know. Contribution to the stated research area will increase the embedding capacity. For measuring the longest path, the tie is broken by comparing x coordinates and then, if required, y coordinates. The illustrated result in the result analysis section details the justification of our claim.

Fast Path Planning of Autonomous Vehicles in 3D Environments

Three dimensional path planner is crucial for the safe navigation of autonomous vehicles (AV), such as unmanned aerial vehicles or unmanned underwater vehicles, which operate in three dimensions. In this paper, we develop a novel 3D path planner, which is fast in generating a near-optimal solution path. The planner generates the 3D path considering the size of an AV so that as the AV traverses the constructed path, it does not collide with an obstacle. This paper introduces a 3D path planner with novel concepts, such as a virtual agent and virtual sensors. In order to generate a 3D path to the goal as fast as possible, we let the virtual agent deploy virtual sensors iteratively, such that the connected sensor network can be formed. The constructed sensor network serves as a topological map for the AV, and we find a shortest path from the start to the goal utilizing the network. The virtual agent’s maneuver is biased towards the goal, in order to find a path to the goal as fast as possible. Moreover, the size of the agent is set considering the safety margin of the generated path. Through MATLAB simulations, we demonstrate the outperformance (low computational load and short path length) of our 3D path planner by comparing it with the 3D RRT-star algorithm.

Timoshenko line-element for stability analysis of tapered I-section steel members considering warping effects

Tapered I-section steel members can achieve efficient material utilization, and are economical alternatives to traditional prismatic ones. Current line-element formulations for tapered I-section members may not accurately capture buckling behavior because they typically ignore shear and warping deformations. The research refines the existing line-element formulation for tapered I-section members by directly considering warping deformation. The tapered member’s flexural rigidity is analytically expressed in a stiffness matrix via utilizing a series of tapering factors, which can produce more accurate analysis than those by the conventional stepped-element or the approximate distribution (e.g., linear, parabolic, or cubic) methods. The element formulation is established based upon the Updated-Lagrangian method that permits moderate deformation. The numerical procedure for tracing the solution path is illustrated in detail with a flowchart. Finally, performances of the proposed method, particularly the accuracy of the new element stiffness matrices, are thoroughly assessed by presenting several examples.