Time Sequence Constraint Research Articles

Multi-Domain Collaborative Task Allocation and Conflict Resolution in Unmanned Systems under Complex Constraints

Under the background that multi-domain collaborative tasks with complex constraints executed by heterogeneous unmanned systems, based on a consensus-based bundle algorithm, a consensus-based synergy algorithm is proposed to solve the distributed multi-domain collaborative task planning problem. First, a multi-domain collaborative task allocation model and score evaluation system are established by considering task resource requirement constraints, task timing constraints, and path threat constraints. Second, the time sequence constraints of collaborative tasks are transformed into elastic time window constraints. The bidding method based on collaborative constraints is used for task allocation, and the improved consistency conflict resolution principle is adopted to realize distributed multi-domain collaborative task allocation conflict resolution. Finally, the path planning is coupled to the task allocation process by using the Bezier curve path, and the results of multi-domain collaborative task allocation and path planning are obtained synchronously. Simulation results show that the proposed algorithm can effectively solve the problem of multi-domain collaborative task planning for heterogeneous unmanned systems.

A modified design method of pulse repetition frequency for synthetic aperture radar system based on the single point equivalent squint model

Pulse repetition frequency (PRF) is an essential parameter in synthetic aperture radar (SAR). However, a small PRF value will cause spectrum ambiguity and a large value will increase the computational complexity. In highly squinted SAR imaging with maneuvering platform, the traditional PRF design methods used are airborne and space borne. SAR systems perform with a low precision because of the complex geometric configuration and system deviations. In order to adapt to the maneuvering platform SAR system, a modified design method of PRF is proposed in this paper. First, the azimuth spectrum of echoes is analyzed by establishing the SAR geometry base on the single point equivalent squint (SPES) model. Subsequently, the calculation of azimuth bandwidth is transformed into an optimization problem, and the lower limit of PRF is obtained by the Golden Section Method. Besides, the criteria of spectrum unambiguity and time sequence constraints in PRF design is also discussed in detail. Simulation experiments demonstrate the feasibility and accuracy of the proposed method.

Effect of Prehospital Epinephrine on Outcomes of Out-of-Hospital Cardiac Arrest: A Bayesian Network Approach.

Background The benefit of prehospital epinephrine in out-of-hospital cardiac arrest (OHCA) was shown in a recent large placebo-controlled trial. However, placebo-controlled studies cannot identify the nonpharmacologic influences on concurrent or downstream events that might modify the main effect positively or negatively. We sought to identify the real-world effect of epinephrine from a clinical registry using Bayesian network with time-sequence constraints. Methods We analyzed a prospective regional registry of OHCA where a prehospital advanced life support (ALS) protocol named “Smart ALS (SALS)” was gradually implemented from July 2015 to December 2016. Using Bayesian network, a causal structure was estimated. The effect of epinephrine and SALS program was modelled based on the structure using extended Cox-regression and logistic regression, respectively. Results Among 4324 patients, SALS was applied to 2351 (54.4%) and epinephrine was administered in 1644 (38.0%). Epinephrine was associated with faster ROSC rate in nonshockable rhythm (HR: 2.02, 6.94, and 7.43; 95% CI: 1.08–3.78, 4.15–11.61, and 2.92–18.91, respectively, for 1–10, 11–20, and >20 minutes) while it was associated with slower rate up to 20 minutes in shockable rhythm (HR: 0.40, 0.50, and 2.20; 95% CI: 0.21–0.76, 0.32–0.77, and 0.76–6.33). SALS was associated with increased prehospital ROSC and neurologic recovery in noncardiac etiology (HR: 5.36 and 2.05; 95% CI: 3.48–8.24 and 1.40–3.01, respectively, for nonshockable and shockable rhythm). Conclusions Epinephrine was associated with faster ROSC rate in nonshockable rhythm but slower rate in shockable rhythm up to 20 minutes. SALS was associated with improved prehospital ROSC and neurologic recovery in noncardiac etiology.

Industrial non-routine operation process risk assessment using job safety analysis (JSA) and a revised Petri net

Abstract An important feature of the industrial non-routine operation processes is the time sequence constraint among its steps. Therefore, the main hazard source could be the risk originating from the out-of-sequence scenarios. The traditional job safety analysis (JSA) method does not take account of time sequence constraint and should be adapted to address this problem. A graphical model based on Petri net is defined and further integrated into JSA. A non-routine operation process can be divided into steps according to JSA and then be represented as the basic unit of the revised Petri net. Through the graphical model, abnormal time sequence scenarios and their corresponding abnormal events can be identified. The united method can address time sequence and provide an intuitive and logical risk assessment procedures. The results can also provide effective guidance for workers and supervisors in the non-routine operations process.

Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines

The identification of activity locations in continuous GPS trajectories is an essential preliminary step in obtaining person trip data and for activity-based transportation demand forecasting. In this research, a two-step methodology for identifying activity stop locations is proposed. In the first step, an improved density-based spatial clustering of applications with noise (DBSCAN) algorithm identifies stop points and moving points; then in the second step, the support vector machines (SVMs) method distinguishes activity stops from non-activity stops among the identified stop points. A time sequence constraint and a direction change constraint are applied as improvements to DBSCAN (yielding an improved algorithm known as C-DBSCAN). Then three major features are extracted for use in the SVMs method: stop duration, mean distance to the centroid of a cluster of points at a stop location, and the shorter of distances from current location to home and to the workplace. The proposed methodology was tested using GPS data collected from mobile phones in the Nagoya area of Japan. The C-DBSCAN algorithm achieves an accuracy of 90 % in identifying stop points in the first step, while the SVMs method is 96 % accurate in distinguishing the locations of activity stops from non-activity stops in the second step. Compared to other variants of DBSCAN used to identify activity locations from GPS trajectories, this two-step method is generally superior.

Substation Fault Diagnosis Based on Time Sequence Fuzzy Petri Net

As the 750kV substation has the characteristics like timing inconsistency, dual protection configuration and uncertain diagnosis result, a fault diagnosis method of substation with redundant protection configuration based on time sequence fuzzy Petri nets is proposed. In this method, redundant knowledge about fault component is represented by using two sets of protective information. On that basis, component redundant diagnosis-model based on time sequence fuzzy Petri net is constructed, which can be decomposed into main and redundant subnet-model, in which initial-information credibility is determined by using information-entropy, the time sequence constraint is checked and initial-information credibility is corrected by using the relationship between acted protection and breaker. Compared with fuzzy Petri net diagnosis method taking no account of time sequence factor, the proposed method can not only identify the malfunction information, but also obtain a certain result. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.2528

Research on Span of Manufacturing Task with Time-Sequence Constraint for Manufacturing Collaborative Alliances

For the manufacturing task of manufacturing collaborative alliances, the relationship between manufacturing task programming and manufacturing resources deployment is analyzed and the model for the span of manufacturing task with time sequence constraint is constructed. The problem of span programming of manufacturing task with time sequence constraint is analyzed by integrating qualitative and quantitative methods from production period for the manufacturing task. The mathematical formulations of influential factors and task span are constructed, and the optimal values of task span are obtained. By analyzing the results of quasi-quantitative study, some meaningful results that benefit to programming for collaborative manufacturing task are achieved.

Study on Optimizing Collaborative Manufacturing Chain

For realizing the sharing and optimization deployment of the manufacturing resources, the concept of collaborative manufacturing chain is proposed. For acquiring the optimal collaborative manufacturing chain, a multi-objective optimization model is developed to minimize the comprehensive cost and the whole production load with time-sequence constraint. The simulation results indicate that the proposed model and algorithm are able to obtain satisfactory solutions.

Collaborative Manufacturing Unit Selection Using a Sorting Adaptive Genetic Algorithm in Networked Environment

In order to solve the collaborative manufacturing unit (CMU) selection problem in the networked cooperative manufacturing environment, a sorting adaptive genetic algorithm is proposed. To obtain the optimal executive manufacturing process, the objective function is constructed considering manufacturing cost and product load rate of candidate CMUs under time-sequence constraint. The embedded subtask scheduling procedure in sorting adaptive genetic algorithm is used to ascertain the penalty cost for the tardiness of the task. Finally, a case study is implemented to verify the feasibility of the proposed approach. The results show that the proposed model and algorithm can obtain satisfactory solutions.

Collaborative Manufacturing Unit Selection Using a Sorting Adaptive Genetic Algorithm in Networked Environment

In order to solve the collaborative manufacturing unit (CMU) selection problem in the networked cooperative manufacturing environment, a sorting adaptive genetic algorithm is proposed. To obtain the optimal executive manufacturing process, the objective function is constructed considering manufacturing cost and product load rate of candidate CMUs under time-sequence constraint. The embedded subtask scheduling procedure in sorting adaptive genetic algorithm is used to ascertain the penalty cost for the tardiness of the task. Finally, a case study is implemented to verify the feasibility of the proposed approach. The results show that the proposed model and algorithm can obtain satisfactory solutions.

Multi-objective optimization of collaborative manufacturing chain with time-sequence constraints

To realize the sharing and optimization deployment of manufacturing resources, a concept of collaborative manufacturing chain (CMC) is proposed for the manufacturing of complex products in a networked manufacturing environment. To acquire the optimal CMC, a multi-objective optimization model is developed to minimize the comprehensive cost and the whole production load with time-sequence constraints. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve optimization functions. The optimal solution set of Pareto is obtained. The technique for order preference by similarity to ideal solution (TOPSIS) approach is then used to identify the optimal compromise solution from the optimal solution set of Pareto. Simulation results obtained in this study indicate that the proposed model and algorithm are able to obtain satisfactory solutions.