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This paper presents an evaluation of several types of neural networks for object recognition by means of ultrasonic sensors. Initially, in order to obtain information from the ultrasonic signal, a parametric method is proposed and a set of features is extracted from the ultrasonic echo envelope. Then, it is necessary to evaluate how much information is provided for each characteristic obtained. Therefore, it has been necessary to carry out an analysis in order to detect the most relevant features. Results about information provided for each feature are presented by order of preference. Subsequently, using these features extracted from the echo signal, an experimental set‐up has been carried out in order to highlight the capabilities of different types of neural networks with this information. Finally, results obtained from experimental tests are presented, and the pattern recognition capabilities of each neural network type, using the selected features, are shown.

During the past decades, several researchers have introduced devices that use sonar systems to detect and/or to determine the object location or to measure the distance to an object using reflected sound waves. The purpose of this paper is to use sonar sensor with textile structure and to test it for detection of objects.

In this study, a sonar system based on intelligent textiles approach for detection of objects has been developed. In order to do this, ultrasonic sensor has been integrated to textile structures by using conductive yarns. Furthermore, an electronic circuit has been designed; PIC 16F877 microcontroller unit has been used to convert the measured signal to meaningful data and to assess the data. The algorithm enabling the objects detection has also been developed. Finally, smart textile structure integrated with ultrasonic sensor has been tested for detection of objects.

Beam shape is presented related to identified object and compared with the actual one given in sensor's datasheet in order to test the efficiency of the proposed method of detection. The achieved results showed that the determined beam pattern matches with the actual one given in its datasheet. Therefore, it can be concluded that the integration of sensor was successful.

This is the first time in the literature that a sonar sensor was integrated into textile structure and tested for detection of objects.

Additive manufacturing (AM) has shown its capability in producing complex geometries. Due to the additive nature, the in situ layer-wise inspection of geometric accuracy is essential to making AM reach its full potential. This paper aims to propose a novel automated in-plane alignment and error quantification framework to distinguish the fabrication, measurement and alignment errors in AM.

In this work, a multi-resolution framework based on wavelet decomposition is proposed to automatically align two-dimensional point clouds via a polar coordinate representation and then to differentiate errors from different sources based on a randomized complete block design approach. In addition, a two-stage optimization model is proposed to find the best configuration of the multi-resolution framework.

The proposed framework can not only distinguish errors attributed to different sources but also evaluate the performance and consistency of alignment results under different levels of details.

A sample part with different featured layers, including a simple free-form layer, a defective layer and a layer with internal features, is used to illustrate the effectiveness and efficiency of the proposed framework. The proposed alignment method outperforms the widely used iterative closest point algorithm.

This work fills a research gap of state-of-the-art studies by automatically quantifying different types of error inherent in manufacturing, measuring and part alignment.

Climate change and variability are both a developmental and an environmental issue. Adaptation to climate change and variability has gained a prominent place on global and local policy agendas, evolving from mainly climate risks impacts and vulnerability assessments to mainly adaptation action, imposing new defies to higher education (HE). The purpose of this paper is to introduce the Climate Vulnerability, Impact, and Adaptation (VIA) Network (CliVIA-Net), a South American university-based coalition aimed at achieving a science for/of adaptation.

CliVIA-Net is a collaborative effort by academic groups from across the spectrum of the natural, social and health sciences focused on improving climate VIA on education, research and practice. In consonance with international literature and practices, the network shifted from a discipline-oriented approach to an interdisciplinary and Earth System Science (ESS)-oriented one. It seeks to advance fundamental understanding and participatory practice-oriented research and to develop a problem orientation question/solving answering methodology. A set of cases studies illustrates how CliVIA-Net faces adaptation and sustainability challenges in the twenty-first century.

Focusing on interdisciplinary graduate education, practice-oriented research and problem orientation practice on climate threats which are already threatening the environment, population’s well-being and sustainability, allows for the co-production of knowledge and solutions, as well stakeholders’ buy-in and commitment.

CliVIA-Net draws upon the results of evolving interdisciplinary approaches on global change and VIA education, the research partnership with stakeholders and decision-makers to develop environmental and health outcomes, e.g. vulnerability indicators and scenario planning.

Recent years have seen a technological change, Industry 4.0, in the manufacturing industry. Human–robot cooperation, a new application, is increasing and facilitating collaboration without fences, cages or any kind of separation. The purpose of the paper is to review mainstream academic publications to evaluate the current status of human–robot cooperation and identify potential areas of further research.

A systematic literature review is offered that searches, appraises, synthetizes and analyses relevant works.

The authors report the prevailing status of human–robot collaboration, human factors, complexity/ programming, safety, collision avoidance, instructing the robot system and other aspects of human–robot collaboration.

This paper identifies new directions and potential research in practice of human–robot collaboration, such as measuring the degree of collaboration, integrating human–robot cooperation into teamwork theories, effective functional relocation of the robot and product design for human robot collaboration.

This paper will be useful for three cohorts of readers, namely, the manufacturers who require a baseline for development and deployment of robots; users of robots-seeking manufacturing advantage and researchers looking for new directions for further exploration of human–machine collaboration.

The positioning and grasping of large-size objects have always had problems of low positioning accuracy, slow grasping speed and high application cost compared with ordinary small parts tasks. This paper aims to propose and implement a binocular vision-guided grasping system for large-size object with industrial robot.

To guide the industrial robot to grasp the object with high position and pose accuracy, this study measures the pose of the object by extracting and reconstructing three non-collinear feature points on it. To improve the precision and the robustness of the pose measuring, a coarse-to-fine positioning strategy is proposed. First, a coarse but stable feature is chosen to locate the object in the image and provide initial regions for the fine features. Second, three circular holes are chosen to be the fine features whose centers are extracted with a robust ellipse fitting strategy and thus determine the precise pose and position of the object.

Experimental results show that the proposed system has achieved high robustness and high positioning accuracy of −1 mm and pose accuracy of −0.5 degree.

It is a high accuracy method that can be used for industrial robot vision-guided and grasp location.

The present paper aims to demonstrate the potential of integration between industrial robotics and Lean Manufacturing (LM) approach to increase the efficiency of an assembly line.

Based on a case study performed in an Italian company, this paper reports a comparative analysis of the results produced on a line balancing study involving a semi-automated production line, aided by an industrial robot.

The results suggest the possibility of implementing industrial robotics in line balancing studies highlighting efficiency gains and idle reduction. Further, it also addresses some concepts directly related to industry 4.0, such as collaborative robotics, artificial intelligence, and lean automation.

Line balancing studies may include advanced robotics in order to extend traditional lean practices toward Digital LM.

This study adds contributions to the operational excellence literature, demonstrating the symbiosis between industrial robotics and LM practices.

With the emerging technologies, collecting and processing data about the behaviors of customers or employees in a specific location has become possible. The purpose of this paper is to evaluate existing data collection technologies.

Technology evaluation problem is handled as a multi-criteria decision-making (MCDM) problem. In this manner, a decision model containing four criteria and eight sub-criteria and four alternatives are formed. The problem is solved using hesitant analytic hierarchy process (AHP) and trapezoidal fuzzy numbers (TrFN).

The results show that the most important sub-criteria are: accuracy, quantity, ıntrospective and cost. Decision makers’ evaluate for alternatives, namely wireless fidelity (WiFi), camera, radio-frequency identification and Bluetooth. The best alternative is found as Bluetooth which is followed by WiFi and Camera.

Technology evaluation problem, just like many other MCDM problems are solved using expert evaluations. Thus, the generalizability of the findings is low.

In this paper, technology selection problem has been handled using hesitant AHP for the first time. In addition, the original methodology is extended by using TrFN to represent the expert evaluations in a better way.

Similar to many business processes, waiting times are also essential for health care processes, especially in obstetrics and gynecology outpatient department (GOD), because pregnant women may be affected by long waiting times. Since creating process models manually presents subjective and nonrealistic flows, this study aims to meet the need of an objective and realistic method.

In this study, the authors investigate time-related bottlenecks in both departments for different doctors by process mining. Process mining is a pragmatic analysis to obtain meaningful insights through event logs. It applies data mining techniques to business process management with more comprehensive perspectives. Process mining in this study enables to automatically create patient flows to compare considering each department and doctor.

The study concludes that average waiting times in the GOD are higher than obstetrics outpatient department. However, waiting times in departments can change inversely for different doctors.

The event log was created by expert opinions because activities in the processes had just starting timestamp. The ending time of activity was computed by considering the average duration of the corresponding activity under a normal distribution.

This study focuses on administrative (nonclinical) health processes in obstetrics and GOD. It uses a parallel activity log inference algorithm (PALIA) to produce process trees by handling duplicate activities. Infrequent information in health processes can have critical information about the patient. PALIA considers infrequent activities in the event log to extract meaningful information, in contrast to many discovery algorithms.

Factors influencing management of construction and demolition (C&D) waste within the Iranian context have yet to be investigated. The purpose of this paper is to define and address this knowledge gap, through development of a model to map the associations among the primary factors affecting C&D waste at project, industry and national levels.

A conceptual model is developed based on synthesising the findings of available studies on factors affecting C&D waste with a focus on developing countries. For collecting data, the study drew upon a questionnaire survey of 103 Iranian construction practitioners. The strength and significance of associations among these factors to modify and validate the model were assessed using the structural equation modelling-partial least squares approach.

Major factors affecting C&D waste management and their level of importance were identified at project, industry and national levels. Results clearly showed that the government should review regulations pertaining to C&D waste management and make sure they are implemented properly. The “polluter pays principle” is a useful guide in devising effective policies and regulations for the Iranian context.

This study contributes to the field through presenting the first major study on C&D waste management in Iran. The study provides a picture of C&D waste management status quo in Iran and encapsulates the factors affecting C&D waste management in the Iranian context at different levels within an integrated model. The findings have practical implications for policy makers and construction practitioners in Iran, similar developing economies and foreign firms planning to operate in Iran.