Social networks have been recently employed as a source of information for event detection, with particular reference to road traffic congestion and car accidents. In this paper, we present a ...real-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria; processes tweets, by applying text mining techniques; and finally performs the classification of tweets. The aim is to assign the appropriate class label to each tweet, as related to a traffic event or not. The traffic detection system was employed for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, often before online traffic news web sites. We employed the support vector machine as a classification model, and we achieved an accuracy value of 95.75% by solving a binary classification problem (traffic versus nontraffic tweets). We were also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification problem and obtaining an accuracy value of 88.89%.
Stress can be defined as a state of anxiety (or mental tension) caused by a particular situation. Everybody experiences stress to some level, but how we respond to stress significantly affects our ...well-being. Various events generate anxiety that leads to stress. For example, not having enough time to complete a task or being late are situations where anxiety (and stress) depends on a temporal factor: the scarcity of time. But people also slide into anxiety as they live in a condition that causes them to be tense, independently of time. The studies eliciting anxiety in laboratory settings have less widely considered this variant. This paper presents a proof of concept (PoC) that investigated the possibility of stimulating anxiety without time pressure through a purposely edited horror movie trailer, giving new insights into the emotional experiences evoked by controlled audiovisual stimuli. The PoC comprised an AI-based classifier to detect a person's emotion among anxiety, relaxation, and none of the two based on the galvanic skin response (GSR), photoplethysmogram (PPG), and heart rate (HR), achieving an accuracy higher than 95%. Key application areas include media and marketing, and psychology. Media producers could improve their content to capture the audience better; psychologists could create tailored exposure experiences to promote gradual desensitization to stress triggers.
The 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.
A ‘hybrid’ method is proposed for the quantitative analysis of materials by LIBS, combining the precision of the calibration-free LIBS (CF-LIBS) algorithm with the quickness of artificial neural ...networks. The method allows the precise determination of the samples’ composition even in the presence of relatively large laser fluctuations and matrix effects. To show the strength and robustness of this approach, a number of synthetic LIBS spectra of Cu–Ni binary alloys with different composition were computer-simulated, in correspondence of different plasma temperatures, electron number densities and ablated mass. The CF-LIBS/ANN approach here proposed demonstrated to be capable, after appropriate training, of ‘learning’ the basic physical relations between the experimentally measured line intensities and the plasma parameters. Because of that the composition of the sample can be correctly determined, as in CF-LIBS measurements, but in a much shorter time.
The usual approach to laser-induced breakdown spectroscopy (LIBS) quantitative analysis is based on the use of calibration curves, suitably built using appropriate reference standards. More recently, ...statistical methods relying on the principles of artificial neural networks (ANN) are increasingly used. However, ANN analysis is often used as a ‘black box’ system and the peculiarities of the LIBS spectra are not exploited fully. An a priori exploration of the raw data contained in the LIBS spectra, carried out by a neural network to learn what are the significant areas of the spectrum to be used for a subsequent neural network delegated to the calibration, is able to throw light upon important information initially unknown, although already contained within the spectrum. This communication will demonstrate that an approach based on neural networks specially taylored for dealing with LIBS spectra would provide a viable, fast and robust method for LIBS quantitative analysis. This would allow the use of a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and provide a fully automatizable approach for the analysis of a large number of samples.
•A methodological approach to neural network analysis of LIBS spectra is proposed.•The architecture of the network and the number of inputs are optimized.•The method is tested on bronze samples already analyzed using a calibration-free LIBS approach.•The results are validated, compared and discussed.
We exploit an evolutionary three-objective optimization algorithm to produce a Pareto front approximation composed of fuzzy rule-based classifiers (FRBCs) with different trade-offs between accuracy ...(expressed in terms of sensitivity and specificity) and complexity (computed as sum of the conditions in the antecedents of the classifier rules). Then, we use the ROC convex hull method to select the potentially optimal classifiers in the projection of the Pareto front approximation onto the ROC plane. Our method was tested on 13 highly imbalanced datasets and compared with 2 two-objective evolutionary approaches and one heuristic approach to FRBC generation, and with three well-known classifiers. We show by the Wilcoxon signed-rank test that our three-objective optimization approach outperforms all the other techniques, except for one classifier, in terms of the area under the ROC convex hull, an accuracy measure used to globally compare different classification approaches. Further, all the FRBCs in the ROC convex hull are characterized by a low value of complexity. Finally, we discuss how, the misclassification costs and the class distributions are fixed, we can select the most suitable classifier for the specific application. We show that the FRBC selected from the convex hull produced by our three-objective optimization approach achieves the lowest classification cost among the techniques used as comparison in two specific medical applications.
Low back pain affects one in three workers in the world and is among the biggest causes of absence from work. Almost 75% of back injuries occur when lifting loads. In warehousing, agriculture, and ...construction, for example, workers are continuously handling loads manually. If incorrectly performed, these tasks put the workers at risk of back pain, injuries, and musculoskeletal disorders. Monitoring how the loads are lifted is key to quickly detecting which workers are showing dangerous behaviors, so that they can be (re)trained to perform the task safely, thereby reducing the risk of injury. This article presents a system based on artificial intelligence (AI) that exploits wearable sensors to assess the safety level of workers lifting loads. The system consists of a reflective safety jacket equipped with two barometric altimeters, a triaxial accelerometer, and a triaxial magnetometer. The sensors of the jacket continuously record these signals during the workday. The system then fuses the data from the two barometric altimeters in order to detect when the worker lifted loads. A neural classifier uses the signals recorded by the accelerometer and magnetometer to determine whether or not the task was performed safely. The system was tested on 30 workers and achieved an accuracy of 95.6%.
The 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.
The papers in this special issue focus on artificial intelligence (AI) for efficiency and sustainability in assembly/disassembly industrial processes. Assembly and disassembly lines are the backbone ...of modern manufacturing. These lines transform raw materials into finished products or take apart used products for reuse and recycling. Assembly lines are designed to optimize the process of putting together products, ensuring that each part is in the right place at the right time. Conversely, disassembly lines are designed to reverse this process, breaking down products into their components for reuse or recycling. Improving these lines is becoming more critical with the increasing complexity of products and the need for higher speed and greater efficiency.
Poor posture is becoming more widespread due to the rising number of jobs that require workers to sit for extended hours. Maintaining proper leg positioning is essential for good overall posture and ...long-term health. However, current monitoring methods involve multiple sensors and cameras, leading to discomfort and privacy concerns. This paper presents a privacy-preserving recommendation system using Light Detection and Ranging (LiDAR) to monitor leg positions. The system captures the horizontal leg outline at knee height and extracts domain-specific features, while a stacked autoencoder reproduces the latent representation of the leg outline. The system comprises three modules, including one-class and multi-class support vector machines, to identify 15 leg positions and reject unrecognized ones. Data from 30 healthy volunteers during work activities trained the system. When tested on new participants, the system achieved an accuracy of over 98%. In addition to monitoring leg positions while respecting privacy and ergonomics, our system alerts workers about poor leg positions and generates intuitive dashboards with posture statistics that help safety engineers identify at-risk workers and body parts. This offers a promising solution for improving desk workers' posture and reducing long-term health issues, respecting privacy.
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