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Fuzzy set theory applied to bend sequencing for sheet metal bending

#artificialintelligence

Brake forming is widely applied in the high variety and small batch part manufacturing of sheet metal components, for the bending of straight bending lines. Currently, the planning of the bending sequences is a task that has to be performed manually, involving many heuristic criteria. However, set-up and bend sequencing procedures and knowledge have to be formally formalized and modeled, for the development of computer-aided process planning systems for sheet metal forming. This paper describes the application of fuzzy set theory for the normalization and modeling of the set-up and bend sequencing process for sheet metal bending. A fuzzy-set based methodology is used to determine the optimal bending sequences for the brake forming of sheet metal components, taking into account the relative importance of handling and accuracy.


This secret tool for sneaky vacation planning on the low at work is sneaky and awesome

Mashable

Who among us hasn't had a moment of relief at the office wasting a few minutes of company time planning how best to spend that precious vacation? The numbers back it up, too--according to data from KAYAK, most people are using the travel planning site during office hours, with 57 percent of searches in the U.S. taking place from 8 a.m. to 6 p.m. during the workweek. The site just launched a brand new under-the-radar page design to help wanderlusting office drones plan out their trips at work without risking the wrath of busybody bosses. KAYAK At Work looks just like an Excel doc--but it's actually a fully-functional trip planning page. The setup will let you search for flights and hotels directly on the spreadsheet layout, populating the grids with trip options just like it would a regular KAYAK search page.


iDriveSense: Dynamic Route Planning Involving Roads Quality Information

arXiv.org Machine Learning

Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majority of the present trip planning applications and service providers are enabling their trip planning recommendations relying on shortest paths and/or fastest routes. However, such suggestions may discount drivers' preferences with respect to safe and less disturbing trips. Road anomalies such as cracks, potholes, and manholes induce risky driving scenarios and can lead to vehicles damages and costly repairs. Accordingly, in this paper, we propose a crowdsensing based dynamic route planning system. Leveraging both the vehicle motion sensors and the inertial sensors within the smart devices, road surface types and anomalies have been detected and categorized. In addition, the monitored events are geo-referenced utilizing GPS receivers on both vehicles and smart devices. Consequently, road segments assessments are conducted using fuzzy system models based on aspects such as the number of anomalies and their severity levels in each road segment. Afterward, another fuzzy model is adopted to recommend the best trip routes based on the road segments quality in each potential route. Extensive road experiments are held to build and show the potential of the proposed system.


Hybrid GA and SA dynamic set-up planning optimization

#artificialintelligence

Set-up planning is used to determine the set-up of a workpiece with a certain orientation and fixturing on a worktable, as well as the number and sequence of set-ups and operations performed in each set-up. This paper presents a concurrent constraint planning methodology and a hybrid genetic algorithm (GA) and simulated annealing (SA) approach for set-up planning, and re-set-up planning in a dynamic workshop environment. The proposed approach and optimization methodology analyses the precedence relationships among features to generate a precedence relationship matrix (PRM). Based on the PRM and inquiry results from a dynamic workshop resource database, the hybrid GA and SA approach, which adopts the feature-based representation, optimizes the set-up plan using six cost indices. Case studies show that the hybrid GA and SA approach is able to generate optimal results as well as carry out re-set-up planning on the occurrence of workshop resource changes.


Policy Gradient With Value Function Approximation For Collective Multiagent Planning

Neural Information Processing Systems

Decentralized (PO)MDPs provide an expressive framework for sequential decision making in a multiagent system. Given their computational complexity, recent research has focused on tractable yet practical subclasses of Dec-POMDPs. We address such a subclass called CDec-POMDP where the collective behavior of a population of agents affects the joint-reward and environment dynamics. Our main contribution is an actor-critic (AC) reinforcement learning method for optimizing CDec-POMDP policies. Vanilla AC has slow convergence for larger problems.