1,720,996 research outputs found
TeMA: A Tensorial Memetic Algorithm for Many-Objective Parallel Disassembly Sequence Planning in Product Refurbishment
Full text linkThe refurbishment market is rich in opportunities—the global refurbished smartphones market alone will be $38.9 billion by 2025. Refurbishing a product involves disassembling it to test the key parts and replacing those that are defective or worn. This restores the product to like-new conditions, so that it can be put on the market again at a lower price. Making this process quick and efficient is crucial. This paper presents a novel formulation of parallel disassembly problem that maximizes the degree of parallelism, the level of ergonomics, and how the workers' workload is balanced, while minimizing the disassembly time and the number of times the product has to be rotated. The problem is solved using the Tensorial Memetic Algorithm (TeMA), a novel two-stage many-objective (MaO) algorithm, which encodes parallel disassembly plans by using third-order tensors. TeMA first splits the objectives into primary and secondary on the basis of a decision-maker's preferences, and then finds Pareto-optimal compromises (seeds) of the primary objectives. In the second stage, TeMA performs a fine-grained local search that explores the objective space regions around the seeds, to improve the secondary objectives. TeMA was tested on two real-world refurbishment processes involving a smartphone and a washing machine. The experiments showed that, on average, TeMA is statistically more accurate than various efficient MaO algorithms in the decision-maker's area of preference
A human-centric system combining smartwatch and LiDAR data to assess the risk of musculoskeletal disorders and improve ergonomics of Industry 5.0 manufacturing workers
No full textMore than one in four workers reportedly suffer from back pain worldwide, leading to 264 million work days lost yearly. In the U.S. alone, it causes 100 billion if including decreased productivity and lost wages. The upcoming Industry 5.0 revolution will introduce human-centric manufacturing systems where workers’ well-being comes first while safeguarding their rights: privacy, autonomy, and human dignity. This paper presents a privacy-preserving system based on artificial intelligence that tracks the posture of assembly/disassembly line workers while performing typical standardized tasks on repeat: connecting and separating parts; screwing and unscrewing using an electric screwdriver; tin soldering and unsoldering. The proposed solution assesses the upper-body (dominant arm, dominant shoulder, and trunk) and lower-body (legs) postures according to the ISO 11226 European standard, based on inertial data recorded by a smartwatch and using Laser imaging Detection and Ranging (LiDAR), respectively. These techniques preserve privacy as they collect data that cannot reveal the worker’s identity or sensitive information. Experiments showed that the system recognized a worker’s posture with a mean accuracy close to 98%. The system could help detect bad posture habits and reduce the chances of musculoskeletal disorders while preserving the workers’ privacy, in compliance with the upcoming Industry 5.0 paradigm
EMOGA: a hybrid genetic algorithm with extremal optimization core for multiobjective disassembly line balancing
Full text linkIn a world where products get obsolescent ever more quickly, discarded devices produce million tons of electronic waste. Improving how end-of-life products are dismantled helps reduce this waste, as resources are conserved and fed back into the supply chain, thereby promoting reuse and recycling. This paper presents the Extremal MultiObjective Genetic Algorithm (EMOGA), a hybrid nature-inspired optimization technique for a multiobjective version of the Disassembly Line Balancing Problem (DLBP). The aim is to minimize the number of workstations, and to maximize profit and disassembly depth, when dismounting products in disassembly lines. EMOGA is a Pareto-based genetic algorithm (GA) hybridized with a module based on extremal optimization (EO), which uses a tailored mutation operator and a continuous relaxation-based seeding technique. The experiments involved the disassembly of a hammer drill and a microwave oven. Performance evaluation was carried out by comparing EMOGA to various efficient algorithms. The results showed that EMOGA is faster or gets closer to the Pareto front, or both, in all comparisons
UHF-RFID smart gate: Tag action classifier by artificial neural networks
Full text linkThe application of Artificial Neural Networks (ANNs) to discriminate tag actions in UHF-RFID gate is presented in this paper. By exploiting Received Signal Strength Indicator values acquired in a real experimental scenario, a multi-layer perceptron neural network is trained to distinguish among tags incoming, outgoing or passing the RFID gate. A 99% accuracy can be obtained in tag classification by employing only one reader antenna and independently from tag orientation and typology
A Multiobjective Evolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy-Rule-Based Systems
No full textEvent detection from video using answer set programming
Full text linkUnderstanding of the visual world is not limited to recognizing individual object instances, but also extends to how those objects interact in the scene, which implies recognizing events happening in the scene. In this paper we present an approach for identifying complex events in videos, starting from object detection using a state-of-the-art object detector (YOLO), providing a set of candidate objects. We provide a logic based representation of events by using a realization of the Event Calculus that allows us to define complex events in terms of logical rules. Axioms of the calculus are encoded in a logic program under Answer Set semantics in order to reason and query over the extracted events. The applicability of the framework is demonstrated over the scenario of recognizing car parking on a handicap slot
Mixing Neural Networks and Contextual Analysis in a High-Speed Handwriting Recognizer
No full textThis article presents HACRE, a system for recognizing handwritten amounts on checks, which integrates neural networkalgorithmswith context analysis techniques. In particular, HACRE consists of two major subsystems which are strictly interacting: the former is based on an ensemble of neural networks and carries out a kind of pre-recognition of the individual characters, which is by necessity only approximate; the latter is based on a context analysis module which carries out a lexical analysis of the recognized characters, based on a mutual correlation between the legal and courtesy amounts, which must match exactly. This analysis produces hypotheses which correct the errors made by the neural networks. The proposed system is implemented on an ad-hoc VLSI chip containing an array of dedicated processors tailored to the application, and tightly interconnected to a host personal computer
End-of-life product disassembly with priority-based extraction of dangerous parts
Full text linkThe amount of electronic waste generated in the world is impressive. The USA alone yearly throw away 9.4 million tons of electronic devices: only 12.5% is recycled. One way to reduce this massive impact on the environment is to disassemble these devices with the aim of reusing and recycling as many parts as possible. Disassembling end-of-life products is a complex industrial process that may pose workers at risk because some parts of the product may contain dangerous materials. It is thus crucial to design efficient, sustainable and secure disassembly lines. This paper presents a multi-objective formulation of the Disassembly Line Balancing Problem (DLBP) which promotes efficiency and includes a new objective that increases the level of safety. The efficiency is guaranteed by balancing the idle times of the workstations, and by maximizing the profit and the level of feasibility of a disassembly sequence, which means disassembling the product as much as possible. Safety is maximized by extracting each dangerous part with a priority that is higher the more dangerous the part is. The most dangerous parts can thus be quickly removed from the product, thereby eliminating the exposure to the greatest risks. The disassembly continues with the execution of the tasks that remove the parts that are gradually less dangerous. Along with the DLBP formulation, this paper presents a genetic algorithm purposely designed to solve the problem. Two real-world case studies are discussed which entail the disassembly of a TV monitor and an air conditioner
Integration of web-scraped data in cpm tools: The case of project Sibilla
No full textModern corporate performance management (CPM) systems are crucial tools for enterprises, but they typically lack a seamless integration with solutions in the Industry 4.0 domain for the exploitation of large amounts of data originated outside the enterprise boundaries. In this paper, we propose a solution to this problem, according to lessons learned in the development of project “Sibilla,” aimed at devising innovative tools in the business intelligence area. A proper software module is introduced with the purpose of enriching existing predictive analysis models with knowledge extracted from the Web and social networks. In particular, we describe how to support two functionalities: identification of planned real-world events and monitoring of public opinion on topics of interest to the company. The effectiveness of the proposed solution has been evaluated by means of a long-term experimental campaign
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