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Summary In this paper, we present a software tool capable of predicting the final quality of triple play services by using the most common assessment metrics. The quality of speech and video in network environment is a growing concern of all the internet service providers to carry the multimedia traffic without the excessive delays and losses, which degrade the quality of multimedia as it is perceived by the end users. Prediction mathematical model is based on results obtained from many performed testing scenarios simulating real behavior in the network. Based on the proposed model, speech or video quality is calculated with regard to policies applied for packet processing by routers and to the level of total network utilization. The application cannot only predict QoS parameters but also generate the source code of particular QoS policy setting according to the user interaction and apply the policy to the routers in the network. Contribution of the work consists of a new software tool enables network administrators and designers to improve and optimize network traffic efficiently.

Summary In recent years, the coal mine safety production situation in our country improved year by year, but severe accidents still occurred; the accidents caused great economic loss to the national economy. According to statistical analysis, almost all of the coal mine accidents will expose the hidden danger in before, most of the accidents caused due to safety management not reaching the designated position and the hidden danger management does not take any decision in time. Based on the coal mine safety management holes in our country, the coal mine macro, meso and micro safety management system was established in this paper, which includes meaning and conception of the theories of the macro, meso and micro safety management, and also includes the matching hardware equipment, in order to achieve the hidden danger's closed-loop control and dynamic early warning in the process of coal mine production.

Innovative products are interacting with the users based on smart sensors and algorithms. Logistics as one example is changing when automated guided vehicles are integrated in the process, supporting or even replacing workers. The way these products are interacting with humans and how they react to certain situations, will determine usability and user experience and therefore the success of use. Developing such products is based on innovative concepts that need to be evaluated and refined at early project stages. Using virtual-reality based user scenarios is one adequate option to do so. This paper describes technical as well as study-based approaches on how potential concepts are realized as virtual prototypes and evaluated by real users. It concludes with the evaluation results of a pilot study but also with general limitations and benefits as best practice advice for this kind of virtual prototyping techniques., aw&I Conference, Bd. 3 (2018): innteract2018

Bosporus-Gulf (BoGu) carriers have significantly been growing into tourism destinations such as the Maldives, Seychelles and Mauritius. Their high market share is driving the growth in the local tourism industry but, at the same time, the dependency on these carriers may constitute a major risk. This paper examines the impact of BoGu carrier arrivals on the local tourism industry drawing on multiple linear regression analysis and finds that all countries are dependent on the BoGu carriers. However, the Seychelles and the Maldives are showing a higher effect size than Mauritius. We investigate reasons for these findings and perform a moderation analysis to identify levers to manage the risks of changes in BoGu carrier capacity.

A strong competitive position can not only be regarded as a key success factor for firms, but may also indicate market power. As with operators of other long-distance transport modes, like e.g. train or coach operators, assessing an airline's overall competitive position is not trivial: This is because airlines serve many different, direct and indirect origin-destination (OD) markets between which competition intensity can vary immensely. Hence, airline (or rather airline group) networks compete against each other, meaning that “typical”, e.g. HHI-based competition assessments at, e.g., the national, airport or direct route levels will not be sufficient to get a full picture. This paper presents a modelling approach for an indicator for the individual competitive positions (CP) of airlines and airline groups at the network level. An application to the intra-European market, based on OD data for 2015, indicates a quite comfortable competitive position for low cost carriers like Ryanair, WIZZ Air, FlyBE or Volotea, followed by the major airline groups (Lufthansa, Air France-KLM, IAG) which focus on hub and spoke networks along some low cost activities. The weakest CP's within our sample of the 30 largest European airlines and airline groups are calculated for leisure airlines like Monarch, which recently filed for bankruptcy.

Modern spaceborne synthetic aperture radar (SAR) sensors, such as TerraSAR-X/TanDEM-X and COSMO-SkyMed, can deliver very high resolution (VHR) data beyond the inherent spatial scales (on the order of 1m) of buildings, constituting invaluable data source for large-scale urban mapping. Processing this VHR data with advanced interferometric techniques, such as SAR tomography (TomoSAR), enables the generation of 3-D (or even 4-D) TomoSAR point clouds from space. In this paper, we present a novel and generic workflow that exploits these TomoSAR point clouds in a way that is capable to automatically produce benchmark annotated (buildings/non-buildings) SAR datasets. These annotated datasets (building masks) have been utilized to construct and train the state-of-the-art deep Fully Convolution Neural Networks with an additional Conditional Random Field represented as a Recurrent Neural Network to detect building regions in a single VHR SAR image. The results of building detection are illustrated and validated over TerraSAR-X VHR spotlight SAR image covering approximately 39 km2 - almost the whole city of Berlin - with mean pixel accuracies of around 93.84%.

Significantly outperforming traditional machine learning methods, deep convolutional neural networks have gained increasing popularity in the application of image classification and segmentation. Nevertheless, deep learning-based methods usually require a large amount of training data, which is quite labor-intensive and time-demanding. To deal with the problem in generating training data, we propose in this paper a novel approach to generate image annotations by transferring labels from aerial images to UAV images and refine the annotations using a densely connected CRF model with an embedded naive Bayes classifier. The generated annotations not only present correct semantic labels, but also preserve accurate class boundaries. To validate the utility of these automatic annotations, we deploy them as training data for pixel-wise image segmentation and compare the results with the segmentation using manual annotations. Experiment results demonstrate that the automatic annotations can achieve comparable segmentation accuracy as the manual annotations.

Exploratory visual analysis is often required to assist human operator to understand and interpret Earth Observation (EO) images. Optimal image representation offers cognitive support in discovering relevant facts about the scene with respect to a particular application. This is of crucial importance for training data sets selection in all Machine Learning tasks, particularly in the design of active learning tools for multispectral (MS) EO data. This paper proposes a deep neural network (DNN) based method to compress, learn and reveal the most significant information included in the spectral bands of EO data in support of relevant visualization for image content analysis. The advanced method uses a DNN to discover the most suggestive pseudo-color representation able to highlight the entire MS image content better than the particular 3 bands selection (R, G, B). We propose the use of information theory and the concept of mutual information to rank the spectral bands based on the amount of information contained, by applying the minimum-redundancy-maximum-relevance (mRMR) criterion on a the image so that we obtain the ranked bands. A DNN stacked autoencoder based paradigm is developed in order to extract and compress in three bands the overall information from the MS EO data. The developed method is demonstrated and validated for Sentinel 2 dataset.

As many other small islands and archipelagos, the Canary Islands depend to a high degree on energy imports. Despite its small surface, the archipelago has a high potential for renewable energy (RE) technologies. In this paper, we present a scenario pathway to a 100% RE supply in the Canary Islands by 2050. It relies on a back-casting approach linking the bottom-up accounting framework Mesap-PlaNet and the high resolution power system model REMix. Our analysis shows that locally available technology potentials are sufficient for a fully renewable supply of the islands’ power, heat, and land transport energy demands. To follow the pathway for achieving a carbon neutral supply, expansion of RE technology deployment needs to be accelerated in the short-term and efforts towards greater energy efficiency must be increased. According to our results, an extended linkage between energy sectors through electric vehicles as well as electric heating, and the usage of synthetic hydrogen can contribute notably to the integration of intermittent RE power generation. Furthermore, our results highlight the importance of power transmission in RE supply systems. Supply costs are found 15% lower in a scenario considering sea cable connections between all islands.

Ultra-wideband (UWB) is a promising technology for positioning systems that has undergone massive research development in recent years. Most UWB positioning systems assume prior knowledge on the positions of the UWB anchors. Without knowing the UWB anchor positions, an accurate position estimate of an UWB tag is difficult. Hence, this paper presents a novel cooperative pedestrian simultaneous localization and mapping (SLAM) algorithm using an UWB positioning system without the necessity to have the prior information on the UWB anchor positions. To obtain accurate estimates of the UWB anchor positions, the estimations of the UWB anchor positions are shared between different mobile units. An increased accuracy of the estimated UWB anchor position relates directly to a more accurate position estimate of the mobile unit position. We evaluate the proposed algorithm based on measurements with multiple mobile units and fixed anchors with unknown positions. The evaluations show that an accurate position estimation of both the mobile units and the anchors is possible without any prior knowledge on the UWB anchor positions.

Today’s voice-based air-ground communica-tion system for aircraft guidance is suffering from increasing saturation of the VHF band in high den-sity areas. Therefore the European Union strives for a transition from analog voice communication to more spectrum efficient digital communication. This transition shall be realized, among others, through the development and implementation of the L-band Digital Aeronautical Communications System (LDACS). In order to verify the suitability of LDACS for both communications and navigation, a flight trial campaign will be performed within the nationally funded German project MICONAV. The objective of this paper is to present the planned LDACS measurement campaign. LDACS will be validated under realistic conditions: Four ground stations transmit signals; one airborne station onboard an aircraft will receive the communication messages and additionally utilize the signals from the ground stations for navigation.

By the electrification of the drive train, new degrees of freedom also result for the design of new suspension concepts. Especially through the combination of mechanical and electrical components, potentials for future mobility can be raised. These new opportunities offer the potential for electric lightweight suspensions, include integrating drive units into the chassis, creating space through new packaging variations, reducing unsprung mass and incorporating individual wheel drives to apply new driving strategies. However, the processes, methods and process models involved in the development of electric vehicles, drive trains and chassis must be systematically supplemented during the development and applied in the product development process. A possible development approach, which ranges from concept development through optimization of vehicle dynamics and mechanical design to the prototype validation of a concept, was developed within the framework of the project “Lightweight, Energy-efficient Integrative Chassis with Hub-motor Technology” [1, 2, 3, 4]. In particular, the paper presents the development process, the software tools used to develop the mechatronic suspension concept and the prototypical realization and detailing.

Multifunctionalization of fiber-reinforced composites, especially by adding energy storage capabilities, is a promising approach to realize lightweight structural energy storages for future transport vehicles. Compared to conventional energy storage systems, energy density can be increased by reducing parasitic masses of non-energy-storing components and by benefitting from the composite meso- and microarchitectures. In this paper, the most relevant existing approaches towards multifunctional energy storages are reviewed and subdivided into five groups by distinguishing their degree of integration and their scale of multifunctionalization. By introducing a modified range equation for battery-powered electric aircrafts, possible range extensions enabled by multifunctionalization are estimated. Furthermore, general and aerospace specific potentials of multifunctional energy storages are discussed. Representing an intermediate degree of structural integration, experimental results for a multifunctional energy-storing glass fiber-reinforced composite based on the ceramic electrolyte Li1.4Al0.4Ti1.6(PO4)3 are presented. Cyclic voltammetry tests are used to characterize the double-layer behavior combined with galvanostatic charge–discharge measurements for capacitance calculation. The capacitance is observed to be unchanged after 1500 charge–discharge cycles revealing a promising potential for future applications. Furthermore, the mechanical properties are assessed by means of four-point bending and tensile tests. Additionally, the influence of mechanical loads on the electrical properties is also investigated, demonstrating the storage stability of the composites.

Humans will soon need to adapt to a collaborative setting in which technology becomes a smart collaboration partner that works with a group to achieve its goals. It is therefore time for collaboration researchers to explore the vast opportunities afforded by smart technology and to test its utility for enhancing team processes and outcomes. In this paper, we take a long view on the implications of smart technology for collaboration process design, and propose a research agenda for the next decade of collaboration research. We create a reference model to frame the research agenda.

With the advent of Smart Grid systems, the digitalization of electrical grid infrastructures aims to improve energy saving and efficiency. The International Electrotechnical Commission (IEC) is one of the organizations that create and manage norms and standards in areas related to electricity and electronics, such as IEC 61850. In recent years, the research community have proposed mappings of different communication protocols to IEC 61850. However, most of the proposals focused on heavyweight interaction paradigms and protocols such as Common Object Request Broker Architecture (CORBA), Data Distribution Service (DDS), HTTP-REST or Web Services. With the proliferation of the Internet of Things (IoT), new lightweight protocols are appearing opening new perspectives to the standard. Hence, this paper firstly presents a validation of a mapping of the IEC 61850 standard to the Constrained Application Protocol (CoAP) and then compares its performance implementing the IEC's mapping against HTTP-REST and Web Services/SOAP. For comparison purposes, the communication latency, the number of total bytes sent, and the size of the overhead are presented for each of the three approaches. To conclude, future perspectives on the suitability of lightweight protocols to the IEC 61850 are provided.

This multi-disciplinary research paper should help solar power plant developers to perform an advanced site assessment in arid locations where the annual irradiance levels are high, but significant quantities of airborne sand and dust increase the risk of optical energy losses due to extinction, soiling, erosion damage (also known as abrasion), etc. Due to these effects sandstorms have a direct consequence on the operation and maintenance (O&M) costs. The work presented in the following characterizes airborne sand and dust material and later focuses on the resulting erosion effects. Some important meteorological and geological parameters for sandstorm occurrence and the resulting erosive damage on glass materials by impacting windblown material are extracted from literature. The respective parameters have been measured at two locations in Morocco (Zagora and Missour). After evaluation of wind and humidity data and a comprehensive soil analysis, the erosion risk was estimated to be higher in Zagora. The specular reflectance loss of exposed silvered-glass reflectors of 5.9% in Zagora and 0.8% in Missour after 25 months of exposure verified this estimation. Additionally, a specular reflectance analysis on a mirror sample that has been exposed for nine months in Kuwait is shown. On that sample specular reflectance losses of more than 40% were measured. A checklist with seven items is given in the conclusion to help solar plant developers to evaluate the risk of component aging due to sand storm erosion.

Purpose Agile software development helps software producing organizations to respond to manifold challenges. While prior research focused on agility as a project or process phenomenon, the authors suggest that agility is an emergent phenomenon on the team level. The paper aims to discuss this issue. Design/methodology/approach Using the theory of complex adaptive systems (CASs), the study captures the multiple influencing levels of software development teams (SDTs) and their interplay with self-organization and emergence. The authors investigate three agile SDTs in different contextual environments that participate with four or more different roles each. Findings The results suggest self-organization as a central process when understanding team agility. While contextual factors often provide restriction on self-organization, they can help the team to enhance its autonomy. Research limitations/implications The theoretical contributions result from the development and test of theory grounded propositions and the investigation of mature agile development teams. Practical implications The findings help practitioners to improve the cost-effectiveness ratio of their team’s operations. Originality/value The study provides empirical evidence for the emergence of team agility in agile SDTs. Using the lens of CAS, the study suggests the importance of the team’s autonomy.

To bridge the gap between users' expectations and technological solutions, a better understanding of human body experience and sensory-motor skills is mandatory. This could pave the way towards a novel generation of robotic hands, which can be successfully employed in everyday life e.g. in prosthetics and assistive robotics. Available robotic hands are still far from matching the requirements of the corresponding experimental and real-world applications, e.g. fast motions might be achieved at the expense of accuracy. Knowledge of the users' sensory-motor skills can guide technical developments, e.g. prosthetic design processes. This paper presents design solutions developed in a Delphi study. Explorative questionnaires are prepared to acquire and elaborate expert opinions to improve the design of previously developed robotic anthropomorphic hands. By gathering and fusing expert opinions, novel robotic hand and wrist concepts specifically optimized regarding body experience and sensory-motor skill ...

This paper presents the development, implementation, and validation of a simplified dynamic modeling approach to describe solid oxide fuel cell gas turbine (SOFC/GT) hybrid systems (HSs) in three real emulator test rigs installed at University of Genoa (Italy), German Aerospace Center (DLR, Germany), and National Energy Technology Laboratory (NETL, USA), respectively. The proposed modeling approach is based on an experience-based simplification of the physical problem to reduce model computational efforts with minimal expense of accuracy. Traditional high fidelity dynamic modeling requires specialized skills and significant computational resources. This innovative approach, on the other hand, can be easily adapted to different plant configurations, predicting the most relevant dynamic phenomena with a reduced number of states: such a feature will allow, in the near future, the model deployment for monitoring purposes or advanced control scheme applications (e.g., model predictive control). The three target systems are briefly introduced and dynamic situations analyzed for model tuning, first, and validation, then. Relevance is given to peculiar transients where the model shows its reliability and its weakness. Assumptions introduced during model definition for the three different test rigs are discussed and compared. The model captured significant dynamic behavior in all analyzed systems (in particular those regarding the GT) and showed influence of signal noise on some of the SOFC computed outputs.

This paper explores how the functional safety of industrial deployments can be improved through emerging Industrie 4.0 approaches. We discuss how new sources of data, that are becoming accessible through advancing digitalization, can be used for this purpose, and how principles from predictive maintenance systems can be applied to industrial fail-safe applications: based on data from the industrial components themselves and from their environment as well as on metadata about interactions between these systems and people, we propose to create a model-based monitoring and controlling system that focuses on preserving the functional safety of the installation as a whole. We expect such a Predictive Fail-Safe system to mitigate or even prevent unsafe consequences of failures even in highly dynamic “smart factories”, thereby reducing or preventing harm to other equipment, the environment, and the involved people.

This paper treats the problem of tracking the global navigation satellite system's (GNSS) signal phase under ionospheric scintillations and to generate an estimate of the scintillation phase and amplitude. In the case of ionospheric scintillations traditional phase locked loop (PLL) based GNSS receivers have problems to track the signal phase accurately. Moreover, these receivers do not estimate the scintillation phase and amplitude, which is evaluated in ionospheric monitoring applications. Therefore, Kalman filter based phase tracking algorithms have been proposed, which track line-of-sight (LOS) phase and scintillation amplitude and phase separately with different state space models. For modeling the scintillation phase and amplitude auto-regressive (AR) models can be used. In this case the parameters for the AR model have to be estimated together with the LOS and scintillation phase and amplitude. We propose to use a second Kalman filter for the estimation of the AR model parameters. This Kalman filter takes the scintillation phase and amplitude estimates from the first Kalman filter as measurement and the AR model parameters as system state. Simulation results for the global positioning system (GPS) show that the proposed algorithm achieves the same estimation performance for scintillation phase and amplitude as an algorithm with a priori knowledge on the AR model parameters.

Evidenced based planning is a salient issue and evokes questions about the role of expertise and scientific policy advice in spatial planning. The paper considers recent trends in the debate on scientific policy advice (crises of expertise, privatization/democratization of expertise, blurring of boundaries between science and policy) and reflects upon these trends against the background of the particular situation of scientific policy advice in spatial planning in Germany. Many insights from the wider debate on policy advice also hold true for spatial planning. However, spatial planning, at least in Germany, reveals some particularities. The scientific foundation of spatial planning is interdisciplinary and the gap between science and practice is probably less visible compared to other scientific disciplines. This makes dualistic approaches (science ��� policy boundaries) questionable. At the same time recent trends towards Gesellschaftsberatung (advice for society), transformative science and transdisciplinarity are attractive directions in spatial planning., disP - the planning review;Vol. 53. Issue 4, p 21-32

Parabolic trough collectors for concentrating solar sower plants are large scale optical devices with demanding requirements on optical and mechanical properties. Accurate mirror shape and absorber tube alignment are necessary to harness solar radiation with high efficiency. There are several methods to assess the shape of the mirror surface, yet there exist few approaches to effectively measure the position of the absorber tube. This paper provides a comprehensive overview on causes and effects of absorber tube displacement and on state of the art measurement techniques. A new approach on fully automated airborne absorber tube position measurement for parabolic trough collectors is presented, which outperforms existing methods concerning speed, spatial resolution, and level of automation, thereby achieving an accuracy of about 1.5 mm in vertical and lateral direction.

International audience; Because of the cloud-induced variability of the solar resource, the growing contributions of photovoltaic plants to the overall power generation challenges the stability of electricity grids. To avoid blackouts, administrations started to define maximum negative ramp rates. Storages can be used to reduce the occurring ramps. Their required capacity, durability, and costs can be optimized by nowcasting systems. Nowcasting systems use the input of upward-facing cameras to predict future irradiances. Previously, many nowcasting systems were developed and validated. However, these validations did not consider aggregation effects, which are present in industrial-sized power plants. In this paper, we present the validation of nowcasted global horizontal irradiance (GHI) and direct normal irradiance maps derived from an example system consisting of 4 all-sky cameras (“WobaS-4cam”). The WobaS-4cam system is operational at 2 solar energy research centers and at a commercial 50-MW solar power plant. Besides its validation on 30 days, the working principle is briefly explained. The forecasting deviations are investigated with a focus on temporal and spatial aggregation effects. The validation found that spatial and temporal aggregations significantly improve forecast accuracies: Spatial aggregation reduces the relative root mean square error (GHI) from 30.9% (considering field sizes of 25 m2) to 23.5% (considering a field size of 4 km2) on a day with variable conditions for 1 minute averages and a lead time of 15 minutes. Over 30 days of validation, a relative root mean square error (GHI) of 20.4% for the next 15 minutes is observed at pixel basis (25 m2). Although the deviations of nowcasting systems strongly depend on the validation period and the specific weather conditions, the WobaS-4cam system is considered to be at least state of the art.

Summary The acceleration and deceleration of trains are essential for satisfactory performance of train services and for effective utilisation of line capacity by railways for a given network. However, it increases the risk of unbalancing the passengers. Therefore, in this paper, the jerk was analysed in the vehicle in order to examine its effect on safety and comfort to the passengers. For the purpose of this analysis, a rake with 24 ICF coaches fitted with CBC (H type tight lock) and low preload draft gear hauled by WDP4 locomotive was considered. It was modelled using Universal Mechanism – a multibody dynamic software. Also, in addition, the vehicle is considered to be subjected to external forces such as rolling resistance, longitudinal wheel resistance, and gravitational force. The tractive and braking efforts for the locomotives are also considered with realistic track conditions between Lucknow and Kanpur of India. The presented results suggest that the CBC with balanced type draft gear reduces the jerk in the train.

Summary Burst pressure is the pressure at which vessel burst/crack and internal fluid leaks. An accurate prediction of burst pressure is necessary in chemical, medical and aviation industry. Burst pressure is a design safety limit, which should not be exceeded. If this pressure is exceeded it may lead to the mechanical breach and permanent loss of pressure containment. So burst pressure calculation is necessary for all the critical applications. To numerically calculate burst pressure material curve is essential. There are various material models which are used to define material curve, amongst them Ramberg–Osgood is very popular. Ramberg–Osgood accurately capture material curve in strain hardening region. This approach is applicable for different material grades. In this paper a finite element method is used to predict burst pressure using Ramberg–Osgood equation. These results are then compared with results obtained from elasto-plastic curve and true stress strain curve. Results obtained by finite element analysis are validated with experimental data which is considered from open literature.

Summary The laying and commissioning of new transmission line is very difficult due to socio-economic problems, like environmental clearances, right of way, etc. Therefore, there is an emphasis on better utilization of available transmission infrastructure. FACTS devices can provide reactive power compensation, transmission capability enhancement, and voltage and stability improvement. FACTS devices operate under the command of system operator who analyses its demand by the data acquired through traditional SCADA system, state estimation algorithms and PMUs. SCADA together with PMU give accurate information about the operational state of power system. This paper proposes a scheme to automate the FACTS devices in collaboration with PMUs in a more efficient way. Highly precised data from PMUs can be fed to intelligent controllers for effective analyzing and automating the FACTS device through control command. Thus, this combination can provide real time control of reactive power, together with enhancement of power handling capability and stability improvement.

Summary The customer's review plays an important role in deciding the purchasing behaviour for online shopping as a customer prefers to get the opinion of other customers by observing their opinion through online products’ reviews, blogs and social networking sites, etc. The customer's reviews reflect the customer's sentiments and have a substantial significance for the products being sold online including electronic gadgets, movies, house hold appliances and books. Hence, extracting the exact features of the products by analyzing the text of reviews requires a lot of efforts and human intelligence. In this paper we intend to analyze the online reviews available for books and extract book-features from the reviews using human intelligence. We have proposed a technique to categorize the features of books from the reviews of the customers. The extracted features may help in deciding the books to be recommended for readers. The ultimate goal of the work is to fulfil the requirement of the user and provide them their desired books. Thus, we have evaluated our categorization method by users themselves, and surveyed qualified persons for the concerned books. The survey results show high precision of the features categorized which clearly indicates that proposed method is very useful and appealing. The proposed technique may help in recommending the best books for concerned people and may also be generalized to recommend any product to the users.

Summary The objective of the paper is to analyse parametric studies and optimum steam extraction pressures of three different (subcritical, supercritical and ultra-supercritical) coal fired power plant cycles at a particular main steam temperature of 600 °C by keeping the reheat temperature at 537 °C and condenser pressure at 0.09 bar as constant. In order to maximize the heat rate gain possible with supercritical and ultra-supercritical steam conditions, eight stages of feed water heater arrangement with single reheater is considered. The system is optimized in such a way that the percentage exergetic losses are reduced for the increase of the exergetic efficiency and higher fuel utilization. The plant cycles are simulated and optimized by using Cycle Tempo 5.0 simulation software tool. From the simulation study, it is observed that the thermal efficiency of the three different power plant cycles obtained as 41.40, 42.48 and 43.03%, respectively. The specific coal consumption for three different power plant cycles are 0.56, 0.55 and 0.54 Tonnes/MWh. The improvement in feed water temperatures at the inlet of steam generator of respective cycles are 291, 305 and 316 °C.

Summary Network-on-Chip has been a growing design paradigm with the rise in Multi-Processor System on Chip (MPSoCs) primarily due to its scalability. While regular meshes (2 or 3-dimensional) are the usual proposal for such a paradigm, a real chip may not follow it. Heterogeneous cores, hardware failures or manufacturing defects can possibly cause irregular topologies in a Network-on-Chip. Selection of a routing algorithm is an important challenge in NoC design as it affects power consumption, communication latency and overall system performance. Routing can be supported in such faulty environment by use of routing tables. But this is not a scalable solution as table size grows with network size. Logic Based Distributed Routing (LBDR) is proposed as a new routing implementation technique which offers compact routing implementation and fault tolerance without use of routing table. In this paper we propose a Hybrid Fault Tolerant Routing Algorithm (HFTRA), which aims to provide fault tolerance support in presence of on-chip link failures. Proposed routing is implemented with LBDR scheme. Analysis of the method has shown the effectiveness of proposed scheme as compared to routing tables when implemented using LBDR.

Summary The world today is experiencing an exponential growth in cyberspace. Nevertheless, India too has witnessed a significant ascend in Internet activities and it is quite assertive to say that such phenomenal growth in access to information on one hand leads to empowered individuals and organization and on the other hand also poses new challenges to government and citizens. To make the cyber world safe is the need of the hour. Putting up deterrent measures against cybercrime is essential to national cyber security in protecting critical infrastructure of the nation as well as for individuals. In this regard, the prime objective of the government is to prevent cyber attacks and to protect the country's critical infrastructure. It also focuses on reducing vulnerability to cyber attacks so as to reduce and minimize damage and recovery time. To prevent the cyber crimes, individuals and governments need to clearly understand the crime schemes in the cyberspace and the contemporary and continuing Internet trends and behaviours of these criminals. This paper gives a brief outline of categories of cybercrimes. These crimes are categorized as crimes against individuals, property, organizations and governments. Various Internet crime scheme are evaluated and behaviour of criminals to perform the cybercrimes has been analyzed. A critical evaluation of report of cybercrime complaints under IT Act 2000 has been presented.

Summary Selection of a suitable robotic manipulator, out of the available conventional configurations, is one way to employ a robot. However, with the increasing applications of robotic assistance, the concept of task-oriented customised design for prescribed working locations in a given environment, has got attention of several researchers. One of the challenges in this direction is the realisation of specific designs, involving unconventional values of robotic parameters. This paper discusses the D–H parameters based adaptive modules, which can be adjusted according to the given values of robotic parameters. The modules are available in three types with similar architecture. The details of this basic length unit, adaptive twist unit and extended length unit are discussed. Focus of the work is to validate the adaptability of the proposed architecture through the modelling, simulation and development of standard configuration.

Summary Selecting the right vendor is an important business decision made by any organization. The decision involves multiple criteria and if the objectives vary in preference and scope, then nature of decision becomes multiobjective. In this paper, a vendor selection problem has been formulated as an intutionistic fuzzy multiobjective optimization where appropriate number of vendors is to be selected and order allocated to them. The multiobjective problem includes three objectives: minimizing the net price, maximizing the quality, and maximizing the on time deliveries subject to supplier's constraints. The objection function and the demand are treated as intutionistic fuzzy sets. An intutionistic fuzzy set has its ability to handle uncertainty with additional degrees of freedom. The Intutionistic fuzzy optimization (IFO) problem is converted into a crisp linear form and solved using optimization software Tora. The advantage of IFO is that they give better results than fuzzy/crisp optimization. The proposed approach is explained by a numerical example.

Active control of structural sound radiation is a promising technique to overcome the poor passive acoustic isolation performance of lightweight structures in the low–frequency region. Active structural acoustic control commonly aims at the suppression of the far–field radiated sound power. This paper is concerned with the active control of sound radiation into acoustic enclosures. Experimental results of a coupled rectangular plate-fluid system under stochastic excitation are presented. The amplitudes of the frequency-independent interior radiation modes are determined in real-time using a set of structural vibration sensors, for the purpose of estimating their contribution to the acoustic potential energy in the enclosure. This approach is validated by acoustic measurements inside the cavity. Utilizing a feedback control approach, a broadband reduction of the global acoustic response inside the enclosure is achieved.

Extinction profile (EP) is an effective feature extraction method which can well preserve the geometrical characteristics of a hyperspectral image (HSI) and by extracting the EP from first three independent components (ICs) of an HSI, three correlated and complementary groups of EP features can be constructed. In this paper, an EPs fusion (EPs-F) strategy is proposed for HSI classification by exploring spatial-spectral information within and among three EP features. In general, the EPs-F method includes two stages. In the first stage, within each EP feature, a superpixel-based composite kernel strategy is proposed to adaptively fuse the spatial information of EP and the spectral feature of HSI. Then, the obtained adaptive composite kernel is used to create a classification map for each EP. In the second stage, decision fusion is further applied on different classification maps to create the final classification result. Experiments on two real HSIs verify the effectiveness of the proposed EPs-F algorithm.

This paper compares different similarity measures for the matching of very-high-resolution SAR and optical images over urban areas. It is meant to provide guidance about the performance of both signal-based and descriptor-based similarity measures in the context of this non-trivial case of multi-sensor correspondence matching. Using an automatically generated training dataset, thresholds for the distinction between correct matches and wrong matches are determined. It is shown that descriptor-based similarity measures outperform signal-based similarity measures significantly.

A traffic data collection system using wireless radio frequency (RF) technologies, e.g. Bluetooth or Wi-Fi, allows for area wide measurement of traffic. This includes travel times on the whole road network. This paper aims to present the technical overview of a newly developed ITS system called DYNAMIC. This system supports indirect data acquisition by moving detectors, processing and provision of floating observer information on the basis of mobile wireless RF data. DYNAMIC consists of sensor hardware and software components like data collection, data import, transport mode detection, data processing, data fusion and visualization.

We present two advances in representing variable renewables (VRE) in global energy-economy-climate models: accounting for region-specific integration challenges for eight world regions and considering short-term storage. Both advances refine the approach of implementing residual load duration curves (RLDCs) to capture integration challenges. In this paper we derive RLDCs for eight world regions (based on region-specific time series for load, wind and solar) and implement them into the REMIND model. Therein we parameterize the impact of short-term storage using the highly-resolved model DIMES. All RLDCs and the underlying region-specific VRE time series are made available to the research community. We find that the more accurate accounting of integration challenges in REMIND does not reduce the prominent role of wind and solar in scenarios that cost-efficiently achieve the 2 °C target. Until 2030, VRE shares increase to about 15–40% in most regions with limited deployment of short-term storage capacities (below 2% of peak load). The REMIND model's default assumption of large-scale transmission grid expansion allows smoothening variability such that VRE capacity credits are moderate and curtailment is low. In the long run, VRE become the backbone of electricity supply and provide more than 70% of global electricity demand from 2070 on. Integration options ease this transformation: storage on diurnal and seasonal scales (via flow batteries and hydrogen electrolysis) and a shift in the non-VRE capacity mix from baseload towards more peaking power plants. The refined RLDC approach allows for a more accurate consideration of system-level impacts of VRE, and hence more robust insights on the nature of power sector decarbonization and related economic impacts.

Despite recent advances in prosthetics and assistive robotics in general, robust simultaneous and proportional control of dexterous prosthetic devices remains an unsolved problem, mainly because of inadequate sensorization. In this paper, we study the application of regression to muscle activity, detected using a flexible tactile sensor recording muscle bulging in the forearm (tactile myography—TMG). The sensor is made of 320 highly sensitive cells organized in an array forming a bracelet. We propose the use of Gaussian process regression to improve the prediction of wrist, hand and single-finger activation, using TMG, surface electromyography (sEMG; the traditional approach in the field), and a combination of the two. We prove the effectiveness of the approach for different levels of activations in a real-time goal-reaching experiment using tactile data. Furthermore, we performed a batch comparison between the different forms of sensorization, using a Gaussian process with different kernel distances.

A major research area in remote sensing is the problem of multi-sensor data fusion. Especially the combination of images acquired by different sensor types, e.g. active and passive, is a difficult task. Over the last years deep learning methods have proven their high potential for remote sensing applications. In this paper we will show how a deep learning method can be valuable for the problem of optical and SAR image matching. We investigate the possible of conditional generative adversarial networks (cGANs) for the generation of artificial templates. Contrary to common template generation approaches for image matching, the generation of templates using cGANs does not require the extraction of features. Our results show the possibility of realistic SAR-like template generation from optical images through cGANs and the potential of these templates for enhancing the matching of optical and SAR images by means of reliability and accuracy.

This paper discusses characteristic multi-disciplinary issues related to quiet short take-off and landing for civil transport aircraft with a typical short to medium range mission. The work reported here is focussing on the noise aspects and is embedded in the collaborative research centre CRC880 in Braunschweig, Germany. This long term aircraft Research initiative focusses on a new transport aircraft segment for operation on airports with shorter runway length in commercial air transport. This calls for a community-friendly aircraft designed for operations much closer to the home of its passengers than today. This Scenario sets challenging, seemingly contradictory aircraft technology requirements, namely those for extreme lift augmentation at low noise. The Research Centre CRC880 has therefore devised a range of technology projects that aim at significant noise reductions and at the generation of e�cffient and flexible high lift. The research also addresses flight Dynamics of aircraft at takeoff and landing. It is envisaged that in general significant noise reduction -compared to a reference turbofan driven aircraft of year 2000 technology- necessarily requires component noise reduction in combination with a low noise a/c concept. Results are presented from all the acoustics related projects of CRC880 which cover the aeroacoustic simulation of the source noise reduction by flow permeable materials, the characterization, development, manufacturing and operation of (porous) materials especially tailored to aeroacoustics, new UHBR turbofan arrangements for minimum exterior noise due to acoustic shielding as well as the prediction of jet noise vibration excitation of cabin noise by UHBR engines compared to conventional turbofans at cruise.

The purpose of this paper is helping managers and leaders in healthcare develop viable organizations that deliver high-level services. Health organizations such as hospitals are exceedingly complex. Therefore, we use Integrative Systems Methodology, a framework designed especially as an enabler for coping with complexity. Within that framework, we combine quantitative and qualitative methods to describe and explain organizational phenomena evolving over time. Rather than a large survey, we use a real-life case study. A single-case setting has been chosen, to enable long-term and in-depth exploration. The case spans 30 years, covering the evolution of the oncological care system of Carinthia, which is a federal state of Austria. The contribution of the chapter is in providing deep insights. It lays open the structures underlying the viability of health organizations. The chapter also provides well-grounded advice for how to build a robust health organization in a context of complexity and change.

The side-looking imaging geometry of synthetic aperture radar (SAR) causes inevitable layover in SAR images. Separating the contributions from different scatterers has been the fundamental for many applications. It is typically solved by explicit inversion of the SAR imaging model to retrieve the scattering profile along the mixed dimension (elevation), which is otherwise known as SAR tomography. This paper proposed a robust blind scatterer separation method to demix the layovered scatterers, avoiding the computationally expensive tomographic inversion. We demonstrate that the state-of-the-art principle component decomposition-based methods are heavily influenced by the nonergodicity of the selected samples, especially in urban area, such as point scatterers appearing often on facades. The proposed method is shown to be more robust than the state-of-the-art. Real data example shows that the proposed method outperforms the state-of-the-art by a factor of three in terms of the accuracy of the retrieved phase.

In this paper, we present a method for regularizing noisy 3D reconstructions, which is especially well suited for scenes containing planar structures like buildings. At horizontal structures, the input model is divided into slices and for each slice, an inside/outside labeling is computed. With the outlines of each slice labeling, we create an irregularly shaped volumetric cell decomposition of the whole scene. Then, an optimized inside/outside labeling of these cells is computed by solving an energy minimization problem. For the cell labeling optimization we introduce a novel smoothness term, where lines in the images are used to improve the regularization result. We show that our approach can take arbitrary dense meshed point clouds as input and delivers well regularized building models, which can be textured afterwards.

Deformation monitoring by multi-baseline synthetic aperture radar (SAR) interferometry is so far the only imaging-based method to assess millimeter-level deformation over large areas from space. Past research mostly focused on optimal deformation parameters retrieval on a pixel-basis. Only until recently, the first demonstration of object-based urban infrastructures monitoring by fusing SAR interferometry (InSAR) and the semantic classification labels derived from optical images was presented in [1]–[3]. This paper proposes an algorithm for object-based joint InSAR deformation reconstruction using these classification labels. We derive an object-based multi-baseline InSAR reconstruction model, and propose an efficient algorithm for bridge detection in optical images.

Within the European funded project ECOMISE a new approach for composite manufacturing is developed. This approach provides key technologies for industry 4.0 in order to maximize process efficiency at reduced cost and time while maintaining structural requirements. In detail, process simulation methods, online process monitoring systems as well as methods for in situ structural evaluation and process adjustment in case of process deviations are implemented and linked via databases. This paper describes the new overall concept as well as the specific in situ structural evaluation approach, exemplarily applied to the fibre deposition process. Prior to manufacturing typical manufacturing features such as locally varying fibre orientation, gaps and overlaps are studied based on given knowledge from previous manufacturing as well as from process simulation. The effect of selected features on the structural properties is investigated for the expected parameter ranges. The real detected features are provided by an online monitoring system during the fibre deposition process. Based on these results an in situ structural evaluation of detected features is performed already during manufacturing in combination with a decision making with respect to required part correction. The developed key technologies and tools for the in situ evaluation process are presented, and their prototype application is shown during manufacturing of an aeronautic wing cover demonstrator.

This paper describes a new analysis method for orthotropic cylindrical shell structures with arbitrary stiffness distribution. The structural model consists of ring frame elements, orthotropic membrane panels and longitudinal stiffeners (stringer). The element stiffness matrix of the orthotropic panel is based on the displacements functions of its boundary only. The boundary value problem is solved by developing the stresses in Taylor series and applying the principal of virtual work (virtual forces). By this way it is possible to have large elements able to describe stress peaks. The method is applied for Booster load introduction into launcher structures and reduces the number of degrees of freedom drastically, allowing rapid analyses necessary for parametric optimization in the pre-design phase.

This paper proposes a new algorithm, which uses the second order information of a Least Absolute Shrinkage and Selection Operator (LASSO) to achieve an active sensing approach driven by minimizing the entropy of sparse unknown environments, for the multi agent case. For this, a signal model, which restricts the agent's measurements according to its sensor's view, is introduced into the Distributed LASSO (DLASSO) framework. With the help of Compressed Sensing (CS), the DLASSO is able to estimate the environment with less measurements. After the DLASSO converged to a solution, each agent evaluates the proposed algorithm for choosing new measurement locations.

The phenomenon of crowdsourcing has emerged as a new pattern of digitally mediated collaboration. This novel socio-technical arrangement changes the organization of work as well as its general nature and takes place in information systems (IS) in which humans face many threats to their dignity. For this reason, the importance of ethical issues within this new form of employment arises. Hence, in this paper we focus on the ethical issues in crowd work – a perspective that has been largely neglected by current crowdsourcing research. We analyze recent crowdsourcing literature and extract ethical issues by following the PAPA (privacy, accuracy, property and accessibility of information) concept, a well-established approach in IS. The review focuses on the individual perspective of crowdworkers, which addresses their working conditions and benefits. Although, the literature review exhibits that there are PAPA dimensions in crowdsourcing, only few focus on the crowdworkers as individuals. Our findings contribute to further research in crowdsourcing by introducing an ethical framework and give practical insight into how to design sustainable and ethical crowd work.

GNSS/IMU navigation systems offer low-cost and robust solution to navigate UAVs. Since redundant measurements greatly improve the reliability of navigation systems, extensive researches have been made to enhance the efficiency and robustness of GNSS/IMU by additional sensors. This paper presents a method for integrating reference data, images taken from UAVs, barometric height data and GNSS/IMU data to estimate accurate and reliable pose parameters of UAVs. We provide improved pose estimations by integrating multi-sensor observations in an EKF algorithm with IMU motion model. The implemented methodology has demonstrated to be very efficient and reliable for automatic pose estimation. The calculated position and attitude of the UAV especially when we removed the GNSS from the working cycle clearly indicate the ability of the purposed methodology.