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This paper tackles the task of singulating and grasping paper-like deformable objects. We refer to such tasks as paper-flipping. In contrast to manipulating deformable objects that lack compression strength (such as shirts and ropes), minor variations in the physical properties of the paper-like deformable objects significantly impact the results, making manipulation highly challenging. Here, we present Flipbot, a novel solution for flipping paper-like deformable objects. Flipbot allows the robot to capture object physical properties by integrating exteroceptive and proprioceptive perceptions that are indispensable for manipulating deformable objects. Furthermore, by incorporating a proposed coarse-to-fine exploration process, the system is capable of learning the optimal control parameters for effective paper-flipping through proprioceptive and exteroceptive inputs. We deploy our method on a real-world robot with a soft gripper and learn in a self-supervised manner. The resulting policy demonstrates the effectiveness of Flipbot on paper-flipping tasks with various settings beyond the reach of prior studies, including but not limited to flipping pages throughout a book and emptying paper sheets in a box. The code is available here: https://robotll.github.io/Flipbot/. © 2023 IEEE.

Pencil (graphite) on paper (PoP) electronics has gained significant attention due to its low cost and versatility in developing customizable disposable electronics. Despite the convenience of the PoP methodology in device fabrication its application in developing electronics is limited due to the absence of a bandgap in graphite. The present work is the first demonstration of the direct writing of 2D MoS2 trace on paper which was utilized as a pressure and strain sensor. Detailed characterization studies revealed the formation of rhombohedral and hexagonal phased MoS2 micro-flowers. XPS studies revealed the formation of mixed 1T (metallic) and 2H (semiconducting) phases of MoS2. With the increase in the number of traces of 2D MoS2 on paper, the resistance of the channel decreased owing to the increase in conducting channels. The as-fabricated pressure sensor displayed a sensitivity of 2.21 kPa-1 and strain sensor displayed a GF of 13. Also, the sensor displayed an excellent durability for 1600 cycles. The superior response of the sensor can be attributed to the synergistic properties of micro fibrous paper substrate and piezo-dependent electron transfer of 2D-MoS2. The presented technique enables 'make it yourself' multifunctional device that have numerous potential applications in the field of consumer electronics and personal healthcare monitoring.

Pencil (graphite) on paper (PoP) electronics has gained significant attention due to its low cost and versatility in developing customizable disposable electronics. Despite the convenience of the PoP methodology in device fabrication its application in developing electronics is limited due to the absence of a bandgap in graphite. The present work is the first demonstration of the direct writing of 2D MoS2 trace on paper which was utilized as a pressure and strain sensor. Detailed characterization studies revealed the formation of rhombohedral and hexagonal phased MoS2 micro-flowers. XPS studies revealed the formation of mixed 1T (metallic) and 2H (semiconducting) phases of MoS2. With the increase in the number of traces of 2D MoS2 on paper, the resistance of the channel decreased owing to the increase in conducting channels. The as-fabricated pressure sensor displayed a sensitivity of 2.21 kPa-1 and strain sensor displayed a GF of 13. Also, the sensor displayed an excellent durability for 1600 cycles. The superior response of the sensor can be attributed to the synergistic properties of micro fibrous paper substrate and piezo-dependent electron transfer of 2D-MoS2. The presented technique enables 'make it yourself' multifunctional device that have numerous potential applications in the field of consumer electronics and personal healthcare monitoring.

Data Analytic (DA) is currently a very important approach to obtain the best possible benefit of the data, but its degree of difficulty compared to other computational processes limits its implementation in organizations. In addition, it remains a term highly confused with respect to other areas of data science, such as data mining, and Big Data. There are few methodologies for its implementation, in addition, its complexity makes it non-participatory, with a certain degree of resistance on the part of the users. The knowledge and skills of users must be exploited when analyzing and producing the business intelligence necessary for the Data Analytic execution. In this position paper, we explore the possibilities of contribution through a Methodology for Data Analytic, which is extended with the incorporation of features extracted from a user-centered design (UCD) approach inspired by the ISEA methodology. This existing DA methodology, called MIDANO, will be extended using gamification techniques, to facilitate the applicability and understanding, in order to guarantee a participation of organizational actors, resulting in a Participatory Data Analytic Methodology. © 2020 IEEE.

Pencil (graphite) on paper (PoP) electronics has gained significant attention due to its low cost and versatility in developing customizable disposable electronics. Despite the convenience of the PoP methodology in device fabrication its application in developing electronics is limited due to the absence of a bandgap in graphite. The present work is the first demonstration of the direct writing of 2D MoS2 trace on paper which was utilized as a pressure and strain sensor. Detailed characterization studies revealed the formation of rhombohedral and hexagonal phased MoS2 micro-flowers. XPS studies revealed the formation of mixed 1T (metallic) and 2H (semiconducting) phases of MoS2. With the increase in the number of traces of 2D MoS2 on paper, the resistance of the channel decreased owing to the increase in conducting channels. The as-fabricated pressure sensor displayed a sensitivity of 2.21 kPa-1 and strain sensor displayed a GF of 13. Also, the sensor displayed an excellent durability for 1600 cycles. The superior response of the sensor can be attributed to the synergistic properties of micro fibrous paper substrate and piezo-dependent electron transfer of 2D-MoS2. The presented technique enables 'make it yourself' multifunctional device that have numerous potential applications in the field of consumer electronics and personal healthcare monitoring.

Currently, we are witnessing an exponential growth in the amount of data being generated and captured at multiple locations. This trend will continue over the next years. Hence, we have envisioned a scenario in which many objects will be referencing to or generating location information. Thus, the need for appropriately managing geospatial data is evident. In this paper, we present our vision for an integral Geo Linked Data platform; pointing out the current limitations and challenges in the GeoRDFization, Storage, Query Federation, and Visualization of data with an inherent spatial context.

A new dynamic simulator for pulp and paper applications has been developed. The simulator is useful for process optimization and operator training, especially when constructing a new paper machine, or rebuilding an existing one, or introducing new employees. The main advantage of the simulator is that the same control system (Distributed Control System source code) and the same displays in the Operator stations are used in the simulator as in the real plant. In this way process operators get very realistic training of various events. Optimization of an existing process line can be done by testing different ways of operation, and then selecting the best way of operation. A suitable rebuild of an existing mill could be found in a similar manner, i.e. by simulating different rebuild possibilities. A DCS functionality checkout can be done before startup of a real mill, since identical DCS code is used in the simulator as in the real mill., Export Date: 7 October 2020; Conference Paper

The paper discusses a framework for managing the ICT-enabled planning of the service production process in a Public Administration (PA). PAs deliver a vast amount of services to citizens, each characterized by a production cost and a customer value. The production cost is the effort needed for the service production, it is influenced by several factors, e.g. By the degree of reuse in the service production process among services that are related by similarities. The customer value of services is usually measured as the trade off between benefits the service provides to customers and sacrifices customers have to deal with. In this paper we deal with a typical problem a PA must face in planning the service production process, namely to decide whether to privilege for a given budget efficiency (maximization of the number of services produced within the budget) or else efficacy (maximization of the overall customer value of services produced within the budget). The framework proposed in the paper is made up of (i) a model for the representation of a repository of services, (ii) a model for the definition of a service portfolio, and (iii) different approaches to be used in decision making for the achievement of efficiency vs. efficacy service production objectives. Two real life case studies are discussed to provide evidence of the impacts of the proposed framework.

Dielectric response measurements are an important maintenance and diagnostic tool for large oil filled power transformers. Regardless of if the measurements being performed in the time domain or in the frequency domain, the developed tools rely on the dielectric response being linear with excitation voltage magnitude. This paper details work done in an initial investigation into the measurement of the non-linear dielectric response in oil-paper insulated power transformers using frequency domain measurements. Measurements were performed on two transformers, with non-linear response currents measured at the higher excitation voltage levels. It was found that non-linearity in the dielectric response will result in the generation of harmonics currents particularly 3rd and 5th harmonics. There will also be effects on the fundamental current magnitude due to the higher order dielectric susceptibilities. The higher order susceptibilities will thus be evident in the measured fundamental complex admittance of the dielectric.

Energy audit is a primary step towards a more rational use of primary energy sources in the energy-intensive process such as the production of packaging paper. By the adoption of the Law on efficient use of energy in 2013, energy audits have become mandatory for all industrial companies in Serbia. This paper presents the methodology and results of the one-day energy audit which was conducted in the Paper Factory - Belgrade (FHB). The measures to increase energy efficiency of the production process, together with the quantification of the savings in the primary energy consumption, are proposed based on the results of the study.

Dielectric response measurements are an important maintenance and diagnostic tool for large oil filled power transformers. There are numerous operational disadvantages to performing dielectric response measurements offline, such as the need to disconnect the transformer from the grid, the time taken for the tests and the risk posed to the network. There is therefore a need to develop techniques to perform dielectric response measurements online. This paper presents the results from experimental work conducted in developing a method for performing Frequency Domain Spectroscopy (FDS) measurements on an energized three phase transformer in the laboratory. A chirp excitation voltage signal is used in this work to reduce the measurement time. Extraction of the magnitude and phase information, from the chirp signal, is accomplished using a chirp-matched fitter. Signal injection onto and out of the transformer under test is achieved via neutral connections or “virtual” neutral connections in the set-up. The experimental work establishes the feasibility of using the FDS test on an energized transformer.

Frequency Domain Spectroscopy (FDS) is used to assess the insulation condition of oil-paper power transformers. Dissipation factor is one of the conventional indicators to analyze insulation ageing status. In this paper, the imaginary admittance of the transformers insulation, after removal of the geometric capacitance, is proposed as an alternative indicator to assist in the interpretation of ageing status. Ageing effects on the imaginary admittance are investigated both through simulation results and experimental results.

Wang, K., Gao, S., Wang, Y., Nirmalathas, A., Lim, C., Alameh, K., ... & Skafidas, E. (2017). Four-wave-mixing based silicon integrated optical isolator with dynamic non-reciprocity. In Lasers and Electro-Optics Pacific Rim (CLEO-PR), 2017 Conference on(pp. 1-2). doi:10.1109/CLEOPR.2017.8118702 Available here.

Vehicle manoeuvre analysis is vital for road safety as it helps understand driver behaviour, traffic flow, and road conditions. However, classifying data from in-vehicle acquisition systems or simulators for manoeuvre recognition is complex, requiring data fusion and machine learning (ML) algorithms. This paper proposes a hybrid approach that combines multivariate multiscale entropy (MMSE) and one-dimensional convolutional neural networks (1D-CNNs). MMSE is utilised for early feature extraction and data fusion, and the extracted features are classified using 1D-CNNs, achieving an impressive 87% test accuracy in multiclass classification. This paper provides insights into improving vehicle manoeuvre classification using advanced ML techniques and data fusion methods to handle complex data sets effectively. Ultimately, this approach can enhance the understanding of driver behaviour, inform policy decisions, and develop more effective strategies to enhance road safety.

On-device transfer learning suggests fine-tuning pretrained neural networks on new input data directly on edge devices. The memory limitation of edge devices necessitates using memory-efficient fine-tuning methods. Fine-tuning involves two primary phases: the forward-pass phase and the backwardpass phase. The forward-pass phase generates output activations, and the backward-pass phase computes gradients and updates the parameters accordingly. Although the forward-pass phase demands a temporary memory to store a layer’s input and output activations, the backward-pass phase may require storing the output activations from all layers to compute gradients. This fact introduces the memory cost of the backward-pass phase as the main contributor to the huge training memory demands of deep neural networks (DNNs), which has been the focus of many studies. However, little attention has been made to how the temporary activation memory involved in the forward-pass phase may also act as the memory bottleneck, which is the main focus of this paper. This paper aims to mitigate this memory bottleneck by pruning unimportant channels from layers that require significant temporary activation memory. Experimental results demonstrate how the proposed method effectively reduces peak activation memory and total memory costs of MobileNetV2 by 65% and 59%, respectively, at the cost of 3% accuracy drop.

Artificial intelligence (AI) has recently been used as a tool for various visual storytelling, but text-to-image models are a stochastic machine learning process that requires human intervention to assist creation better. As we all know, pre-visualization is an important stage in film production, involving subjective choices by the creators. This paper investigates how AI can assist filmmakers during the pre-production stage by generating mood boards from text. We propose a novel preproduction pipeline and guidelines that leverage text-to-image models to create visual previews of film projects. We also conduct a case study to validate and evaluate our approach's effectiveness. Our case study suggests that following the guidelines we have developed can assist filmmakers in generating mood boards that effectively convey the desired atmosphere of their projects and potentially contribute to enhancing the creative process. Our paper aims to contribute to the field of AI art and the film industry. © 2023 IEEE.

The metabolic rate has attracted widespread interest because it is the most important parameter in thermal comfort assessment. Nevertheless, it is often determined inaccurately. This paper establishes a novel metabolic rate model, which we believe is the first of its kind to achieve straightforward metabolism evaluation via wearable technology. The theory starts from the root of the metabolic rate, combining a new heat-loss calculation approach, which, together with heart rate, skin resistance, and body muscle rate, finally results in a linear model. In addition, a novel metabolic rate sensor is designed and invented based on the proposed theory to achieve a miniature, real-time, user-friendly, comfortable measuring device. Three environmental temperature conditions (22, 25 & 28 ) under four physical activity stages are involved in this paper's experiments. The remarkable results demonstrate that a linear relationship is established and verified between the metabolic rate (0 ~ 3.5 Mets) tested by the instrument and the newly proposed model. A high coefficient of determination exists at all temperature conditions, showing at least 95% accuracy and less than 2% uncertainty. The most notable finding is that the proposed model is deterministic for fixed objects, regardless of the experimental temperature conditions. This research focuses on wearable metabolic rate sensor measurement. Further still, the innovative approach and the designed sensor can play a constructive role in thermal comfort evaluation. © 2023 University of Split, FESB.

In an erasure-coded distributed storage system (DSS), requesting a file requires downloading information from multiple storage nodes, called servers, which leads to cross-server network traffic. The cross-server transmission cost can be reduced if these servers are equipped with extra memory to cache some information about the files. This paper considers the so-called cache-aided DSS (CADSS), where each server is connected to several caching proxies through a shared link, and studies the transmission cost incurred by file requests. For simplicity, we focus on a CADSS in which the servers are connected via a one-hop link, and each server is connected to the same number of caching proxies. When the caching proxies receive file requests, a server first downloads some symbols from the other servers, called the helper servers, and then broadcasts some symbols to the caching proxies. For a single server, the maximum number of symbols downloaded from the helper nodes (respectively broadcast to its caching proxies) normalized by the file size is called the cross-server (respectively local) reads. This paper first optimizes the cross-server and local reads separately. Whether from the perspective of optimizing cross-server or local reads, a CADSS can be interpreted as an equivalent single-server caching system but with different system parameters. This paper analyzes the optimal tradeoff between the cross-server and local reads. It is shown that the optimal cross-server and local reads can be achieved simultaneously for some parameters, while a tradeoff exists for some other parameters. We characterize the two extreme points of the optimal tradeoff curve and derive the optimal tradeoff for some specific parameters. © 2023 IEEE.

This paper aims to efficiently save energy consumption of multiple-input-single-output (MISO) networks. To this end, an optimization problem is formulated to minimize the transmit power of the base station (BS) while ensuring the minimal required information rate of each user. Reinforcement learning (RL) is a significant solution to tackle this non-convex problems. Nevertheless, designing efficient reward functions that accurately describe the involved optimization problems for RL is a challenging problem, which may result in low learning efficiency and even inaccurate learning. To cope with such an issue, this paper proposes an inverse reinforcement learning (IRL)-based approach to minimize the transmit power of MISO networks, in which the generative adversarial imitation learning (GAIL) is employed as the IRL framework and the reward function is represented by an artificial neural network (ANN) model, whose parameters are learned automatically in terms of an expert policy based on the semidefinite relaxation (SDR)-based approach. Since SDR is able to achieve the near optimal solution, the learned reward function is more accurate than traditional artificially designed one. Simulations show that, the proposed IRL-based approach reaches similar energy consumption to the SDR-based approach, and reduces about 50% transmit power compared to traditional RL-based one.

Understanding the needs and constraints of systems in general and a system-of-systems in particular can be challenging, yet crucial. Relying only on upfront activities will not be sufficient. Important information can be gathered around the performance and behavior of the system as well as stakeholder needs in operation. A digital twin is a way to model and understand the operation of a system. To understand the challenges and enablers related to digital twins in a system-of-systems context, we performed a literature study. In total, only 10 papers were identified that explicitly address this topic, all from the last five years, indicating that this is an active field of research. The papers revealed that definitions and terminology are unclear and that similar challenges as for systems-of-systems also exist for systems-of-digital twins. The complexity and dynamic nature of systems-of-systems motivate further study of digital twins to understand needs and constraints. However, key challenges such as concepts and principles of digital twins for systems-of-systems, cost and benefits, and evolution needs to be better understood.

Technical advances as well as continuously evolving business demands are reshaping the need for flexible connectivity in industrial control systems. A way to achieve such needs is by using a service-oriented approach, where a connectivity service middleware provides controller as well as protocol-specific interfaces. The Message Queuing Telemetry Transport (MQTT) protocol is a widely used protocol for device-to-device communication in the Internet of Things (IoT). However it is not commonly integrated in industrial control systems. To address this gap, this paper describes the development and implementation of a prototype of a connectivity service middleware for MQTT within an industrial private control network. The prototype implementation is done in the context of an industrial controller, and used in a simulated modular automation system. Furthermore, various deployment scenarios are evaluated with respect to response time and scalability of the connectivity service., Conference paper; Export Date: 26 July 2023; Cited By: 0; Conference name: 12th Mediterranean Conference on Embedded Computing, MECO 2023; Conference date: 6 June 2023 through 10 June 2023; Conference code: 190017

Cybersecurity is an important concern in systems-of-systems (SoS), where the effects of cyber incidents, whether deliberate attacks or unintentional mistakes, can propagate from an individual constituent system (CS) throughout the entire SoS. Unfortunately, the security of an SoS cannot be guaranteed by separately addressing the security of each CS. Security must also be addressed at the SoS level. This paper reviews some of the most prominent cybersecurity risks within the SoS research field and combines this with the cyber and information security economics perspective. This sets the scene for a structured assessment of how various cyber risks can be addressed in different SoS architectures. More precisely, the paper discusses the effectiveness and appropriateness of five cybersecurity policy options in each of the four assessed SoS archetypes and concludes that cybersecurity risks should be addressed using both traditional design-focused and more novel policy-oriented tools.

Multi-core computing platforms have recently been introduced in the vehicle domain to enable novel data-intensive and computation-demanding features in advanced real-time embedded system applications (e.g., a modern car) that could not be supported by traditional single-core computing platforms. Although the existing model-and component-based software development techniques can efficiently deal with the challenge of managing the software complexity in these systems, the core challenge of ensuring their timing predictability comes at the cost of performance loss and underutilization of the systems' resources. This paper identifies a sheer standoff between ensuring timing predictability and performance when the traditional model-and component-based software development techniques are used to develop software architectures of these systems. Note that ensuring timing predictability of a real-time system during its development is as important as ensuring its correct logical functionality. This paper takes the position that the traditional techniques fail to resolve this standoff mainly because they consider the following three viewpoints either in isolation or in pairs, but not altogether: (i) viewpoint of the software architecture modeling, (ii) viewpoint of the execution environment modeling, and (iii) viewpoint of the timing predictability verification. To mitigate this standoff, the paper discusses several approaches and points out opportunities for further research in developing a novel model-and component-based software development framework that jointly considers the three viewpoints during the development of these systems.

The COVID-19 pandemic has induced dramatic effects in all areas of society worldwide. It has led to drastic restrictions on academic exchanges and caused research projects to be put on hold. The need to maintain social distancing has severely affected the research communities that rely on in-person studies. In particular, Quality of Experience (QoE) research relies heavily on user studies and in-person subjective tests as means of obtaining ground truths for system designs. In this position paper, we focus on the impact of COVID-19 on conducting subjective tests for digital media quality assessment. An overview of the related discussions that take place in associated research communities is provided. A number of challenges are posed in terms of hygiene, ethics, standardization, and research quality issues. Opportunities for consolidating QoE research under COVID-19 and beyond are put forward, including alternative experimental designs and adaptation of research methodologies. Numerous enablers are suggested and discussed, revealing various avenues that can be followed to assist in conducting subjective tests under pandemic conditions. We believe that this position paper will be helpful for researchers and practitioners relying on in-person studies to keep abreast about the potential measures that support coming out of the COVID-19 pandemic strong and being prepared for future endeavors. © 2021 IEEE.