Heuristic Information Research Articles

A Game-Learning-Based Smooth Path Planning Strategy for Intelligent Air–Ground Vehicle Considering Mode Switching

Numerous missions in both civil and military fields involve the pursuit-evasion problem of vehicles. With vertical take-off and landing capability, the intelligent air–ground vehicle expands its feasible path to 3-D space, which has great advantages in the pursuit. This vehicle requires adequate path planning to obtain an optimal 3-D path and further improve the pursuit efficiency. The planning process of the air–ground vehicle currently faces the technical difficulties of acquiring the proper takeoff timing and position while optimizing the planning trajectory, especially in a complex environment with dense obstacles. To solve the above issues, a game-learning-based smooth path planning strategy for the intelligent air–ground vehicle considering mode switching is proposed in this article. First, a new reward function of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm, considering the influence of flight obstacle crossing parameters, is presented to explore the short forward track distance. Second, in the update rule, the pursuit-evasion game acts in the mode switching decisions. During interactive learning between the vehicle and environment, this game constantly updates the Nash equilibrium solutions for mode switching and gets a series of switching decisions of the pursuer vehicle (ego vehicle). Third, a double-yaw correction for path smoothing modification is proposed to reduce turning points and avoid local path deviations. This modification provides heuristic information for the exploration of the environment, which significantly speeds up the convergence speed of the algorithm. Finally, the proposed strategy is verified on a 1000 m*1000 m map with 0–200 m obstacle height. Results show that this strategy is effective to decrease the 253-m distance compared with the traditional reinforcement learning algorithm and has a faster convergence speed. The number of trajectory direction changes is 36% less than that of the game-learning algorithm only considering mode switching. The unreasonable large angle turns are eliminated.

A fuzzy logic-based approach for fault diagnosis and condition monitoring of industry 4.0 manufacturing processes

Since the introduction of the industry 4.0 paradigm, manufacturing companies are investing in the development of algorithmic diagnostic solutions for their industrial equipment, relying on measured data and process models. However, process and fault models are not usually available for complex productions plants and production data are usually unlabeled. Thus, to classify machine status, unsupervised approaches such as anomaly detection and signal processing strategies have to be employed. Due to the unsupervised nature of the problem, it is meaningful to apply several diagnostic algorithms to cover most of the process anomalous behaviors. Additionally, in some contexts, the experience of process operators in grasping the correct functioning of machines as well as their ability in understanding early signs of deterioration is relevant for the diagnosis of incoming failures. However, seldom these information can be included in failure diagnosis algorithms. In this paper, we propose a diagnostic scheme for condition monitoring of mechanical components. The proposed scheme combines anomaly detection algorithms, envelope analysis of vibration data, and eventually additional qualitative information on machine functioning. The combination of all the fault indicators is obtained leveraging on a fuzzy inference system. The proposed scheme is experimentally validated on a steel making plant with real process data, making use of heuristic information such monitoring reports of machine health status.

Формування вимог до інформаційного супроводу виробництва аерокосмічних виробів для забезпечення їх якості

The aim of this study is informational support of the aerospace object production at the stages of their lifecycle. The purpose is to provide the necessary parameters of the aerospace products quality, which involves providing relevant information at each stage of the lifecycle in an easy-to-understand and complete form. The task is to create the analytical standard using the example of parts of precision and unit production. The model is based on the analytical standard of parts geometry developed using CAD/CAM-systems with the subsequent expansion of the models list to transfer the necessary information to all participants of the product lifecycle. According to the analysis, significant limitations of drawing documentation for informational support of aerospace industry objects at the stage of technology development are identified. The quality characteristics of the part are grouped according to the compliance degree with its nominal (reference) value of shape and dimensions (macrogeometry and microrelief), physical and chemical properties of the material and the surface layer, technological heredity and industrial purity. It is proposed to develop their models with the CAD/CAM-systems tools based on the analytical sample of geometry. Three necessary conditions for the correct formulation of the shaping problem regarding the implementation of quality management of serial products of machine-building enterprises are established. If at least one of them is violated, it is necessary to attract additional heuristic information to reproduce the correctness of the problem. The analytical standard creation and control implementation of an example of a mill that considers the geometry of the edges are shown, as well as a comparison of the reference geometry and geometry portrait. The scientific novelty of the obtained results consists of the concept introduction of an edge as a surface that connects the working “lines of two surfaces intersection” of a part. The representation of edges in the form of surfaces, not lines, on the one hand, and the selection of these surfaces from the totality of other working surfaces of the parts, on the other hand, creates an informational basis for developing the appropriate manufacturing technologies and maintenance of these parts elements during the product operation.

Risk behavior of schoolchildren: forms, etiology and consequences

The purpose of the article is to present the forms, consequences and etiology of the risky behavior of modern schoolchildren. Methods and objects of research: Defining the concept of risky behavior, the author, analyzing the data of academic primary sources, using technologies of content, contextual, associative and heuristic information analysis, indicates, that the most adequate information about the rating and nature of the social danger of risky behaviors is provided by primary sources that empirically study epidemiological data in the social stratum schoolchildren. The most represented are sociological, personological, therapeutic (clinical), diagnostic, phenomenological areas of research. Presenting the rating of risky forms of behavior of schoolchildren according to the criterion of the scale of epidemiology, the author describes their etiology and possible consequences. Results of research: The material of the article shows that the different nature of the forms of risky behavior of adolescents encourage researchers to take different methodological and methodological positions. This methodological isolation and the subject narrowness associated with it do not yet allow creating a universal tool for diagnosing all the considered forms of adolescent risk behavior.

Adaptive Coordination Ant Colony Optimization for Multipoint Dynamic Aggregation.

Multipoint dynamic aggregation is a meaningful optimization problem due to its important real-world applications, such as post-disaster relief, medical resource scheduling, and bushfire elimination. The problem aims to design the optimal plan for a set of robots to execute geographically distributed tasks. Unlike the majority of scheduling and routing problems, the tasks in this problem can be executed by multiple robots collaboratively. Meanwhile, the demand of each task changes over time at an incremental rate and is affected by the abilities of the robots executing it. This poses extra challenges to the problem, as it has to consider complex coupled relationships among robots and tasks. To effectively solve the problem, this article develops a new metaheuristic algorithm, called adaptive coordination ant colony optimization (ACO). We develop a novel coordinated solution construction process using multiple ants and pheromone matrices (each robot/ant forages a path according to its own pheromone matrix) to effectively handle the collaborations between robots. We also propose adaptive heuristic information based on domain knowledge to promote efficiency, a pheromone-based repair mechanism to tackle the tight constraints of the problem, and an elaborate local search to enhance the exploitation ability of the algorithm. The experimental results show that the proposed adaptive coordination ACO significantly outperforms the state-of-the-art methods in terms of both effectiveness and efficiency.

Variable speed multi-task allocation for mobile crowdsensing based on a multi-objective shuffled frog leaping algorithm

In multi-task studies of mobile crowdsensing, the possibility that a user may adopt another travel mode when completing the current task to the next is ignored. In addition, existing methods tend to allocate more tasks to the users with high reputation, which causes that few tasks will be assigned to new users with low reputation. In order to cover these shortages, a constrained multi-objective optimization model of variable speed multi-task allocation is established, which aims to maximize the user rewards and minimize the task completion time simultaneously. Meanwhile, the maximum number of fully paid tasks positively correlated with reputation is set for each user. To solve the constructed model, a three-stage multi-objective shuffled frog leaping algorithm is proposed, which introduces an objective anchored hybrid initialization operator based on heuristic information, a region mining strategy for the archive individuals, a discrete leaping rule to enhance the interaction of individual information and a constraint handling operator to reduce the loss of individual information. The performance of the proposed algorithm is evaluated by comparing it with five state-of-the-art algorithms on both real-world and synthetic instances. Experimental results show that the proposed algorithm can find a set of Pareto optimal allocation solutions with better convergence and distributions.

Collaborative Multi-Robot Formation Control and Global Path Optimization

For multi-robot cooperative formation and global path planning, we propose to adjust the repulsive field function and insert a dynamic virtual target point to solve the local minima and target unreachability problems of the traditional artificial potential field (APF) method. The convergence speed and global optimization accuracy of ant colony optimization (ACO) are improved by introducing improved state transfer functions with heuristic information of the artificial potential field method and optimizing the pheromone concentration update rules. A hybrid algorithm combining APF and improved ACO is used to calculate an optimal path from the starting point to the end point for the leader robot. A cooperative multi-robot formation control method combining graph theory and consistency algorithm is proposed based on path planning of the leader robot. Taking AGVs in a logistics distribution center as an example, the feasibility of the improved algorithm and its effectiveness in solving the multi-robot path planning problem are verified.

TLNPMD: Prediction of miRNA-Disease Associations Based on miRNA-Drug-Disease Three-Layer Heterogeneous Network.

Many microRNAs (miRNAs) have been confirmed to be associated with the generation of human diseases. Capturing miRNA–disease associations (M-DAs) provides an effective way to understand the etiology of diseases. Many models for predicting M-DAs have been constructed; nevertheless, there are still several limitations, such as generally considering direct information between miRNAs and diseases, usually ignoring potential knowledge hidden in isolated miRNAs or diseases. To overcome these limitations, in this study a novel method for predicting M-DAs was developed named TLNPMD, highlights of which are the introduction of drug heuristic information and a bipartite network reconstruction strategy. Specifically, three bipartite networks, including drug–miRNA, drug–disease, and miRNA–disease, were reconstructed as weighted ones using such reconstruction strategy. Based on these weighted bipartite networks, as well as three corresponding similarity networks of drugs, miRNAs and diseases, the miRNA–drug-disease three-layer heterogeneous network was constructed. Then, this heterogeneous network was converted into three two-layer heterogeneous networks, for each of which the network path computational model was employed to predict association scores. Finally, both direct and indirect miRNA–disease paths were used to predict M-DAs. Comparative experiments of TLNPMD and other four models were performed and evaluated by five-fold and global leave-one-out cross validations, results of which show that TLNPMD has the highest AUC values among those of compared methods. In addition, case studies of two common diseases were carried out to validate the effectiveness of the TLNPMD. These experiments demonstrate that the TLNPMD may serve as a promising alternative to existing methods for predicting M-DAs.

Ship pipe route design using improved multi-objective ant colony optimization

In view of various objectives and complex constraints in ship pipe route design (SPRD), the previous studies often convert the problem into a single objective optimization consisting of several weighted routing objectives, which cannot generate rich references for designers. In addition, the current CAD systems only provide an environment for manual design and simple automatic route design, resulting in low work performance and a huge workload. Therefore, this paper presents a new ship pipe route design method based on multi-objective ant colony optimization (MOACO). Firstly, the mathematical model and multi-objective definition of SPRD are formulated, and then the key technologies of the modified algorithm are introduced in detail, e.g., ant moving direction, heuristic information, pheromone update rules, tabu search and step backward strategy et al. On this basis, the MOACO framework and its internal ant routing process are presented. To route multiple pipes and branched pipes, a combination method based on Pareto solutions and human choice is given. Finally, the feasibility and efficiency of the proposed method are verified by the simulation and actual routing cases.

Noise-Tolerant Fuzzy-$\beta$-Covering-Based Multigranulation Rough Sets and Feature Subset Selection

As a novel fuzzy covering, fuzzy <inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> covering has attracted considerable attention. However, the traditional fuzzy-<inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula>-covering-based rough set and most of its extended models cannot well fit the distribution of samples in real data, which limits their application in classification learning and decision making. First, the upper and lower approximations of these models have no inclusion relation, so they cannot characterize a given objective concept accurately. Moreover, most of these models are hard to resist the influence of noise data, resulting in poor robustness in feature learning. For these reasons, a robust rough set model is set forth by combining fuzzy rough sets, covering-based rough sets, and multigranulation rough sets. To this end, the optimistic and pessimistic lower and upper approximations of a target concept are reconstructed by means of the fuzzy <inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> neighborhood related to a family of fuzzy coverings, and a new multigranulation fuzzy rough set model is presented. Furthermore, a fuzzy dependence function is introduced to evaluate the classification ability of a family of fuzzy <inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> coverings at different granularity levels. The dimensionality reduction of a given fuzzy covering decision table is carried out from the perspective of maintaining the discrimination power, and a forward algorithm for feature selection is developed by using the optimistic significance of candidate features as heuristic information. Three groups of numerical experiments on 16 different types of datasets demonstrate that the proposed model exhibits good robustness on datasets contaminated with noise and outperforms some state-of-the-art feature learning algorithms in terms of classification accuracy and the size of the selected feature subset.

An sEMG-Based Human-Exoskeleton Interface Fusing Convolutional Neural Networks With Hand-Crafted Features.

In recent years, the human-robot interfaces (HRIs) based on surface electromyography (sEMG) have been widely used in lower-limb exoskeleton robots for movement prediction during rehabilitation training for patients with hemiplegia. However, accurate and efficient lower-limb movement prediction for patients with hemiplegia remains a challenge due to complex movement information and individual differences. Traditional movement prediction methods usually use hand-crafted features, which are computationally cheap but can only extract some shallow heuristic information. Deep learning-based methods have a stronger feature expression ability, but it is easy to fall into the dilemma of local features, resulting in poor generalization performance of the method. In this article, a human-exoskeleton interface fusing convolutional neural networks with hand-crafted features is proposed. On the basis of our previous study, a lower-limb movement prediction framework (HCSNet) in patients with hemiplegia is constructed by fusing time and frequency domain hand-crafted features and channel synergy learning-based features. An sEMG data acquisition experiment is designed to compare and analyze the effectiveness of HCSNet. Experimental results show that the method can achieve 95.93 and 90.37% prediction accuracy in both within-subject and cross-subject cases, respectively. Compared with related lower-limb movement prediction methods, the proposed method has better prediction performance.

Graph embedding based ant colony optimization for negative influence propagation suppression under cost constraints

• The idea of swarm intelligence is introduced, and the positive feedback mechanism of ant colony is used to simulate the spread of negative influence. • Graph embedding technology is used to reconstruct the relationship between nodes in the network. • This method can select cost-effective suppression nodes with limited cost, and can effectively prevent the spread of the node’s negative influence in different communities. • This research can play an important role in the fields of epidemic prevention and control, rumor spreading and so on. In the information spreading mechanism of social networks, the influence propagation of information sources often has different effects on different users. How to effectively suppress the negative effects is particularly important. In the case of unknown network propagation principle, this paper introduces the idea of swarm intelligence, which utilizes the positive feedback mechanism of ant colony to simulate the propagation of negative influence, and finds a set of high-value and low-cost suppression nodes. On this basis, the graph embedding technique is used to obtain the new relationships between nodes in the network, and the new relationships between the nodes are used as heuristic information for the ant colony algorithm. Experiments show that our algorithm can not only find the set of inhibitory nodes with limited cost, but also effectively limit the spread of negative influence in the network compared with other algorithms. The research of this paper can not only enrich the theoretical research results of influence maximization, but also play an important role in the analysis of network topology, as well as in the fields of epidemic prevention and control, rumor propagation and so on.

Mobile Robot Path Planning Based on Angle-Guided Ant Colony Algorithm

Ant colony algorithm is easy to fall into local optimum and its convergent speed is slow in solving mobile robot path planning. Therefore, an ant colony algorithm based on angle guided is proposed in this paper to solve the problems. In the choice of nodes, integrate the angle factor into the heuristic information of the ant colony algorithm to guide the ants' search direction and improve the search efficiency. The pheromone differential updating is carried out for different quality paths and the pheromone chaotic disturbance updating mechanism is introduced, then the algorithm can make full use of the better path information and maintain a better global search ability. According to simulations, its global search is strong and it can range out of local optimum and it is fast convergence to the global optimum. The improved algorithm is feasible and effective.

Constraint-based electoral districting using a new compactness measure: An application to Portugal

Since the 1960s, several approaches have been proposed to prevent the redrawing of electoral districts so that they are beneficial to a particular party or political faction (gerrymandering). In order to avoid gerrymandering, most approaches focus solely on redrawing electoral maps that try to maximize the compactness of the electoral districts. However, this problem is computationally hard, and several criteria must be satisfied when drawing electoral maps.In this work we focus on drawing electoral districts for Portugal. For that, a new compact Boolean formulation is proposed for solving the electoral districting problem. This formulation satisfies all standard criteria for building electoral maps, particularly the contiguity and representation criteria. Moreover, a new compactness measure is also proposed that does not depend on geographic centers. Additionally, a heuristic formulation is also devised for problem instances where the optimum values are hard to find.The new compactness measure and formulations are applied in the context of Portuguese parliament elections, assuming a change in the Portuguese electoral system. Experimental results on drawing electoral maps show that the proposed formulations are more effective than previous ones in drawing the electoral districts in the Portuguese context. Moreover, based on results from the last elections, gerrymandering scenarios are devised, showing that electoral outcomes can change depending on the drawing of the electoral maps.

Heuristic Vetoing: Top-Down Influences of the Anchoring-and-Adjustment Heuristic Can Override the Bottom-Up Information in Visual Images.

When making decisions under uncertainty, human subjects do not always act as rational decision makers, but often resort to one or more mental “shortcuts”, or heuristics, to arrive at a decision. How do such “top-down” processes affect real-world decisions that must take into account empirical, “bottom-up” sensory evidence? Here we use recognition of camouflaged objects by expert viewers as an exemplar case to demonstrate that the effect of heuristics can be so strong as to override the empirical evidence in favor of heuristic information, even though the latter is random. We provided the viewers a random number that we told them was the estimate of a drone reconnaissance system of the probability that the visual image they were about to see contained a camouflaged target. We then showed them the image. We found that the subjects’ own estimates of the probability of the target in the image reflected the random information they were provided, and ignored the actual evidence in the image. However, when the heuristic information was not provided, the same subjects were highly successful in finding the target in the same set of images, indicating that the effect was solely attributable to the availability of heuristic information. Two additional experiments confirmed that this effect was not idiosyncratic to camouflage images, visual search task, or the subjects’ prior training or expertise. Together, these results demonstrate a novel aspect of the interaction between heuristics and sensory information during real-world decision making, where the former can be strong enough to veto the latter. This ‘heuristic vetoing’ is distinct from the vetoing of sensory information that occurs in certain visual illusions.

An improved ant colony algorithm based on artificial potential field and quantum evolution theory

The optimal evacuation route in emergency evacuation can further reduce casualties. Therefore, path planning is of great significance to emergency evacuation. Aiming at the blindness and relatively slow convergence speed of ant colony algorithm path planning search, an improved ant colony algorithm is proposed by combining artificial potential field and quantum evolution theory. On the one hand, the evacuation environment of pedestrians is modeled by the grid method. Use the potential field force in the artificial potential field, the influence coefficient of the potential field force heuristic information, and the distance between the person and the target position in the ant colony algorithm to construct comprehensive heuristic information. On the other hand, the introduction of quantum evolutionary theory. The pheromone is represented by quantum bits, and the pheromone is updated by quantum revolving door feedback control. In this way, it can not only reflect the high efficiency of quantum parallel computing, but also have the better optimization ability of ant colony algorithm. A large number of simulation experiments show that the improved ant colony algorithm has a faster convergence rate and is more effective in evacuation path planning.

Route Optimization for E-Commerce Logistics Systems

This paper aims to solve the last-mile distribution of rural e-commerce logistics (RECL) for the survival of third-party logistics enterprise. Considering the features of the RECL (long transport chain and low consumption density), A route optimization model is constructed for RECL's last-mile distribution to maximize the profit of the logistics enterprise, which is subsidized by the government. To solve the model, the ant colony optimization (ACO) was improved to suit the RECL's last-mile distribution by modifying the heuristic information, the update rule of pheromone, and the solution construction. Next, the optimal combinations of the default parameters in the improved ACO were determined through Matlab tests on test datasets in different sizes. The other parameters were configured according to the scale of the RECL. On this basis, the improved ACO was proved effective through example analysis on the said test datasets. The analysis results also react how the number of vehicles affects the maximum profit of the logistics enterprise and the coverage of the RECL logistics network.

A Study on the Relationship between Artificial Intelligence and 5G Network Construction and the Level of Economic Development of Regional Cities

Urban digital governance represented by digital city and smart city is becoming an important direction of urban agglomeration development. It is necessary to reexamine the internal relationship mechanism between local government digital action and regional governance behavior. Based on the theory of collective action and network governance, this paper discusses how the economic development level of local government interacts with the cooperative network status of urban agglomeration and affects the tendency of digital governance strategy of local government. By constructing the configuration matrix of “economic development level” and “cooperative network status” of local governments in urban agglomerations, this paper extracts the possible “center” and “follow” governance strategies that local governments in different configurations may adopt in the digital field. The ant colony optimization algorithm proposed in this paper consists of two parts—ACO initialization and ACO optimization. ACO initialization describes how to map the path of ants to the solution of the problem, that is, the combination of orders. In addition, ACO initialization will also define and initialize the pheromone and heuristic information model compatible with the solution. ACO optimization updates the pheromone model to heuristic guide ants to build paths, so as to iteratively find the optimal solution. The case study of 26 cities in the Yangtze River Delta of China shows that most of the digital governance strategies of local governments are in line with the selection logic of their configuration status of “economic level network status”. The study results proved that the proposed model has provided effective accuracy of 96%. However, driven by selective returns, there are still some “abnormal” governance tendencies.

Early Warning of Basketball Injury Risk Based on Attribute Reduction Algorithm.

Basketball is one of the students' favorite ball games, and it is also one of the most popular sports for college students to carry out after class. Especially in recent years, with the spread of NBA culture around the world and the extensive development of CUBA in domestic colleges and universities, Yao Ming has appeared in China to compete in the NBA and achieve brilliant achievements. With the increasing investment in the number of basketball venues, basketball itself, as a sport with low dependence on venues, equipment, and people, has the characteristics of economy, convenience, and remarkable sports effect compared with other sports. College students' basketball skills, basketball awareness, and love for basketball are increasing day by day. Attribute reduction algorithm is one of the core contents of knowledge discovery, which describes whether every attribute in the attribute set of information system is necessary and how to delete unnecessary knowledge. Based on the attribute reduction algorithm, this paper studies the early warning of basketball injury risk. The basketball injury can not only make athletes unable to participate in training or competition, but even cripple or lose their lives, which hinders the normal development of sports. Therefore, we should make a comprehensive and objective analysis of sports training to find out the causes of sports injuries, so as to prevent sports injuries. This algorithm takes the attribute frequency as the heuristic information and solves the attribute selection problem when the attribute frequency is the same.

Solar Radiation Prediction using Ant Colony Optimization and Artificial Neural Network

This paper proposes a Solar Radiation Prediction Model employing Ant Colony Optimization (ACO) and Artificial Neural Network (ANN), named as SRPM. SRPM aims to incorporate the Feature Selection (FS) technique in the ANN training with the guidance of hybridizing ACO. The main reason behind using FS technique is, it can provide an improved solution from a particular problem by identifying the most salient features from the available feature set. In SRPM, the hybridizing ACO search technique utilizes the information gathered from the correlation among the features and the result of ANN training. To assist the ACO search, pheromone updating technique and measurement of heuristic information have been performed by two particular sets of rules. Thus, SRPM utilizes the benefits of using the wrapper and filter approaches in selecting the salient features during SRP task. The combination eventually creates an equipoise between exploration and exploitation of ants in the way of searching as well as strengthening the capability of global search of ACO for obtaining well qualified solution in SRPM. To evaluate the executive efficiency of SRPM, data samples were collected from BMD and NASA-SSE department. Exploratory outcomes show that SRP can select six utmost salient features by providing 99.74% and 99.78% averaged testing accuracies for BMD and NASA-SSE data samples that are composed of 12 and 15 original features, respectively. Furthermore, the proposed model successfully obtained 0.26% and 0.22% MAPE for BMD and NASA-SSE data samples, respectively with a high correlation of about 99.97% within the actual and predicted data.