Search results

Past research in examining the performance of alternative lot‐sizing rules has focused on the total cost of inventory carrying cost and set‐up cost. Although this cost‐related performance measure is significant for evaluating the overall efficiency of production systems, there are other variations such as frequent rescheduling, generally referred to as system nervousness, occurring that would affect the production scheduling and subsequently the system performance. Expands the performance criteria to re‐evaluate the effectiveness of using several commonly tested lot‐sizing rules in a multi‐level MRP system under stochastic operating environments by means of a simulation study. Results indicate that the Silver‐Meal algorithm seem to perform very well under most operating environments tested. Also, the operating environments play a significant role in the relative performance of lot‐sizing rules tested.

This paper presents the design and validation of a universal 6D seam tracking system that reduces the need of accurate robot trajectory programming and geometrical databases in robotic laser scanning. The 6D seam tracking system was developed in the flexible unified simulation environment, integrating software prototyping with mechanical virtual prototyping, based on physical experiments. The validation experiments showed that this system was both robust and reliable and should be able to manage a radius of curvature less than 200 mm. In the pre‐scanning mode, a radius of curvature down to 2 mm was managed for pipe intersections at 3 scans/mm, using a laser scanner with an accuracy of 0.015 mm.

The primary goal of a startup is to find a viable business model that can generate value for its customers while being effectively captured by the startup itself. This business model, however, is not easily defined, being a consequence of the application of tools involving trials, data analyses and testing. The Lean Startup (LS) methodology proposes a process for agile and iterative validation of business models. Given the popularity and importance of such methodology in professional circles, the purpose of this paper is to conduct a historical literature review of existing academic and professional literature, correlating LS concepts and activities to previous theory and alternative business model validation methods.

A historically oriented systematic literature review employing snowball sampling was conducted in order to identify academic and professional literature and references for iterative validation of business models. A total of 12 scholarly journals and professional magazines dealing with strategy, innovation, entrepreneurship, startups and management were used as data sources. The extensive literature review resulted in 963 exploratory readings and 118 papers fully analyzed.

The results position the LS as a practical-oriented and up-to-date implementation of strategies based on the Learning School of strategy making and the effectuation approach to entrepreneurship; the authors also identify a number of methods and tools that can complement the LS principles.

This paper identified and synthesized the scientific, academic and professional foundations that precede, support and complement the main concepts, processes and methods advocated by the LS methodology.

This paper aims to realize a fully distributed multi-UAV collision detection and avoidance based on deep reinforcement learning (DRL). To deal with the problem of low sample efficiency in DRL and speed up the training. To improve the applicability and reliability of the DRL-based approach in multi-UAV control problems.

In this paper, a fully distributed collision detection and avoidance approach for multi-UAV based on DRL is proposed. A method that integrates human experience into policy training via a human experience-based adviser is proposed. The authors propose a hybrid control method which combines the learning-based policy with traditional model-based control. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the approach.

A fully distributed multi-UAV collision detection and avoidance method based on DRL is realized. The reward curve shows that the training process when integrating human experience is significantly accelerated and the mean episode reward is higher than the pure DRL method. The experimental results show that the DRL method with human experience integration has a significant improvement than the pure DRL method for multi-UAV collision detection and avoidance. Moreover, the safer flight brought by the hybrid control method has also been validated.

The fully distributed architecture is suitable for large-scale unmanned aerial vehicle (UAV) swarms and real applications. The DRL method with human experience integration has significantly accelerated the training compared to the pure DRL method. The proposed hybrid control strategy makes up for the shortcomings of two-dimensional light detection and ranging and other puzzles in applications.

This study aims to realize a sensorless metal object detection (MOD) using machine learning, to prevent the wireless power transfer (WPT) system from the risks of electric discharge and fire accidents caused by foreign metal objects.

The data constructed by analyzing the input impedance using the finite element method are used in machine learning. From the loci of the input impedance of systems, the trained neural network (NN), support vector machine and naive Bayes classifier judge if a metal object exists. Then the proposed method is tested by experiments too.

In the test using simulated data, all of the three machine learning methods show high accuracy of over 80% for detecting an aluminum cylinder. And in the experimental verifications, the existence of an aluminum cylinder and empty can are successfully identified by a NN.

This work provides a new sensorless MOD method for WPT using three machine learning methods. And it shows that NNs obtain high accuracy than the others in both simulated and experimental verifications.

Currently, there are some finite element head models developed by research groups all around the world. Nevertheless, the majority are not geometrically accurate. One of the problems is the brain geometry, which usually resembles a sphere. This may raise problems when reconstructing any event that involves brain kinematics, such as accidents, affecting the correct evaluation of resulting injuries. Thus, the purpose of this study is to develop a new finite element head model more accurate than the existing ones.

In this work, a new and geometrically detailed finite element brain model is proposed. Special attention was given to sulci and gyri modelling, making this model more geometrically accurate than currently available ones. In addition, these brain features are important to predict specific injuries such as brain contusions, which usually involve the crowns of gyri.

The model was validated against experimental data from impact tests on cadavers, comparing the intracranial pressure at frontal, parietal, occipital and posterior fossa regions.

As this model is validated, it can be now used in accident reconstruction and injury evaluation and even as a design tool for protective head gear.

Physical phenomena interact with each other in ways that one cannot be analyzed without considering the other. To account for such interactions between multiple phenomena, partitioning has become a widely implemented computational approach. Partitioned analysis involves the exchange of inputs and outputs from constituent models (partitions) via iterative coupling operations, through which the individually developed constituent models are allowed to affect each other’s inputs and outputs. Partitioning, whether multi-scale or multi-physics in nature, is a powerful technique that can yield coupled models that can predict the behavior of a system more complex than the individual constituents themselves. The paper aims to discuss these issues.

Although partitioned analysis has been a key mechanism in developing more realistic predictive models over the last decade, its iterative coupling operations may lead to the propagation and accumulation of uncertainties and errors that, if unaccounted for, can severely degrade the coupled model predictions. This problem can be alleviated by reducing uncertainties and errors in individual constituent models through further code development. However, finite resources may limit code development efforts to just a portion of possible constituents, making it necessary to prioritize constituent model development for efficient use of resources. Thus, the authors propose here an approach along with its associated metric to rank constituents by tracing uncertainties and errors in coupled model predictions back to uncertainties and errors in constituent model predictions.

The proposed approach evaluates the deficiency (relative degree of imprecision and inaccuracy), importance (relative sensitivity) and cost of further code development for each constituent model, and combines these three factors in a quantitative prioritization metric. The benefits of the proposed metric are demonstrated on a structural portal frame using an optimization-based uncertainty inference and coupling approach.

This study proposes an approach and its corresponding metric to prioritize the improvement of constituents by quantifying the uncertainties, bias contributions, sensitivity analysis, and cost of the constituent models.

Indoor environments are characterized by several pollutant sources. Some of these can be sufficiently characterized through the prediction of the airflow and pollutant distribution patterns. The purpose of this study was to simulate, analyze and compare different locations of known pollutant source inside a ventilated room.

Computational fluid dynamics modelling approach was used to analyze the prediction of the airflow and pollutant distribution patterns for different locations of known pollutant source inside a ventilated room by mixing ventilation.

Distinct areas of poor air quality, perfectly identified by concentration fields, were given. The indoor air quality obtained by the different simulated conditions was analyzed and compared.

Pollutant concentration was not measured in the validation experiments (qualitative validation based on the velocity fields).

Once the contaminant concentration fields are calculated based on the source location, the model is very useful to choose the best place to install any pollutant indoor equipment to preserve breathing zones.

Providing an effective indoor air quality assessment to prevent exposure risk. The results would be useful for making decisions to optimize the design procedure, such as establish the best location to install polluting equipment, occupied areas and their interdependence with ventilation systems. In addition, this tool also helps to choose the best location and correct set point adjustment for the pollutant sensors.

The purpose of this paper is to develop a dress design knowledge base (DDKB), which is expected to be further applied to a personalized dress recommendation system.

Dress design knowledge can be expressed as the relationship between designer's fashion perceptions of different dress elements. In order to extract dress design knowledge, a dress shape ontology (DSO) is firstly developed, which can be further used to form a dress element matrix (DEM). A perceptual descriptive space of the dress (DPDS) is developed for the description of the designer's fashion perception of dress. Through a standard sensory evaluation procedure performed by experienced experts (designers), the expected relationship can be obtained. This relationship is then mathematically simulated by fuzzy logic tools for the expected DDKB.

In this paper, a DDKB has been developed. The established knowledge base has been validated, and it can be further applied to dress recommendation system for a specific consumer.

This study introduces the concept of knowledge base to the area of dress individualized design. The knowledge-based design process based on sensory evaluation and fuzzy logic can efficiently solve the individualization of dress design in traditional design processes, which can provide a novel way to dress design individualization.

This paper is to present an experiment to verify that the motion errors of robust topology optimization results of compliant mechanisms are insensitive to load dispersion.

First, the test pieces of deterministic optimization and robust optimization results are manufactured by the combination of three-dimensional (3D) printing and casting techniques. To measure the displacement of the test piece of compliant mechanism, a displacement measurement method based on the image recognition technique is proposed in this paper.

According to the experimental data analysis, the robust topology optimization results of compliant mechanisms are less sensitive to uncertainties, comparing with the deterministic optimization results.

An experiment is presented to verify the effectiveness of robust topology optimization for compliant mechanisms. The test pieces of deterministic optimization and robust optimization results are manufactured by the combination of 3D printing and casting techniques. By comparing the experimental data, it is found that the motion errors of robust topology optimization results of compliant mechanisms are insensitive to load dispersion.