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The inability to effectively and systematically identify and measure the damage in bridges will lead to an acceleration and dangerous deterioration of the health state of these structures. To repair and replace the aging and damaged bridge infrastructures, and prevent catastrophic bridge collapse, there is an urgent need to develop reliable, innovative, and efficient approaches to the performance assessment and inspection of bridges. Unmanned Aerial Vehicles (UAVs), also knowns as drone, technology has found its way into a number of civilian applications including inspection in the last 20 years, predominantly due to lower cost and tangible scientific improvements. The intent of this paper is to map the current state-of-the-art drone-enabled bridge inspection practices and investigated their associated hazards and risks. This paper will integrate scenario prediction and, assess hazards as well as the social and environmental loss in the case of drone-enabled bridge inspection. Further, this paper will follow rather closely a three-phase process: hazard identification, hazard analysis, and hazard evaluation, all executed with qualitative data and methods by experts of a variety of fields, methodologies for recognition of the impact of cold operating environment on the performance of drone and drone -pilots, creative interpretation of the hazard factors of identifiable problems. The proposed Preliminary Hazard Analysis (PHA) is exemplified via drone-enabled inspection of Håkenby bridge, which is located in the Viken county, in the eastern part of Norway.

Snow avalanches are a significant natural hazard in Norway. One method to manage the backcountry avalanche hazard is through detailed mapping of avalanche terrain. Avalanche terrain can be mapped using a variety of methods, including using the Avalanche Terrain Exposure Scale (ATES); however, manual classification of terrain using ATES is time consuming. This study has developed and compared a fully automated algorithm to provide ATES mapping for all of Norway. Our new algorithm is based on the technical model for ATES mapping. This model has specific terrain-based thresholds that can be applied for automated terrain-based modeling. Our algorithm expands on prior work by including the potential release area (PRA) model to identify and calculate the likelihood of an avalanche releasing from a start zone. We also use the raster-based TauDEM-model to determine the avalanche runout length. The final product is a 10-m resolution ATES map. We compared this nationwide ATES map with areas that have been manually mapped by avalanche experts, and find that the automated approach yields similar and reliable results. In addition to comparing mapped areas, we also examine manually mapped linear routes and compare these with the automated mapped ATES areas. Our results suggest that for open terrain, the vast majority of the manually classified tracks are predominantly in the same ATES class as our algorithm. For forested areas, we get mixed results, which can be attributed to a lack of suitable vegetation data at an appropriate scale. Despite this limitation, the current ATES algorithm and resulting spatial data are already valuable as a large portion (~ 70%) of the Norwegian backcountry terrain is above tree line. The automated algorithm is also useful to ensure consistent manual classification across different regions in Norway, or globally, and will permit greater reproducibility and easier updating of mapping for the future.

Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead.

Adequate thermal protection for passengers travelling on-board vessels in cold climate regions, such as that provided by thermal protective immersion suits (TPIS), enhances passenger survivability in emergency situations, in particular those requiring the abandonment of the vessel. As emergency abandonment is a time critical process, it is essential to consider the time required to correctly don the TPIS. Testing standards, such as the International Maritime Organization guidelines, require that TPIS must be able to be donned within 2 minutes. Unfortunately, current practices quantifying donning times are questionable and so there is a limited evidence base that reliably quantifies donning times required by typical passengers. This paper presents a test procedure designed to reliably quantify the time required by test subjects to don the TPIS. Furthermore, the procedure assesses the donning correctness – a TPIS that is incorrectly donned is unlikely to offer appropriate thermal protection. The paper will also discuss the deficiencies in current practices to assess required donning time.

This work presents a novel approach to detecting stress differences between experts and novices in Situation Awareness (SA) tasks during maritime navigation using one type of wearable sensor, Empatica E4 Wristband. We propose that for a given workload state, the values of biosignal data collected from wearable sensor vary in experts and novices. We describe methods to conduct a designed SA task experiment, and collected the biosignal data on subjects sailing on a 240° view simulator. The biosignal data were analysed by using a machine learning algorithm, a Convolutional Neural Network. The proposed algorithm showed that the biosingal data associated with the experts can be categorized as different from that of the novices, which is in line with the results of NASA Task Load Index (NASA-TLX) rating scores. This study can contribute to the development of a self-training system in maritime navigation in further studies.

Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments.

The topic of transition curves is well known in track and road engineering. Over the years the need for easement between straight and curved sections in railway and road design has grown, with an important reason being increase in vehicle speed. Testing of transitions is often done with graph analysis where the property of lateral change of acceleration is compared between varieties of curves. However, graph analysis does not give a clear understanding of the behaviour of a vehicle in a transition phase the way, for instance, field or laboratory experiments might do. In this paper we present an initial study on the behaviour of a down-scaled vehicle model driving through three distinct transitions from straight to curved sections. In addition we show a comparison of the physical model against a virtual model.

Wind power experienced a substantial growth over the past decade especially because it has been seen as one of the best ways towards meeting climate change and emissions targets by many countries. Since wind power is not fully dispatchable, the accuracy of wind forecasts is a key element for the electric system operators, as it strongly affects the decision-making processes. The planning horizon can be short term (1 -3 months) and long-term (6–12 months) depending on the process.The objective of this paper is to conduct a performance comparison of five deep learning models each combined with three types of data pre-processing and used for short term and long-term multi-variate predictions. The input data are time series of the wind power capacity factor and the temperature. In addition, this paper sets out to demonstrate and review the state-of-the-art deep learning models for prediction with a secondary objective to present the reader a reference point to better understand which model to choose and what factors are significant. The first contribution of this paper is to apply, assess and compare a selection of the novel and cutting-edge deep learning models for multi-variate prediction. Multi-variate predication is achieved through a proposed multiple input and multiple output (MIMO) architecture. Compared to traditional prediction models, machine learning techniques have the advantage of generalization. Among various techniques deep learning is particularly getting more attention due to the applicability to various dataset such as numerical and character. This investigation focuses on five models — Deep Feed Forward (DFF), Deep Convolutional Network (DCN), Recurrent Neural Network (RNN), Attention mechanism (Attention) and Long Short-Term Memory Networks (LSTM). The second contribution is to propose a novel approach to transform the time series dataset to signal for input and reconstruct the model predictions through inverse transformation, by means of the so-called discrete wavelet transformation and fast Fourier transformation. The different models are assessed also by comparing their performance with and without the input dataset manipulation through wavelet and FFT transformation. Beyond that, the model performances are outlined in detail, to give the reader an overview of the models to choose from for short-term or long-term prediction. The results demonstrate that the Attention and DCN perform best with Wavelet or FFT signal, whereas some other models perform better with no data preprocessing. Keywords: Unsupervised machine learning, Multi-variate prediction, Short and long term prediction, Wind power forecasting, Time series to frequency transformation, Performance evaluation and comparison

Due to the unprecedentedly increasing demands of waterway transportation on the Yangtze River during the latest years, traffic congestion at the Three Gorges Dam (TGD) has becoming a serious problem. In busiest seasons, a vessel may need to wait up to 3 days for passing the TGD via the ship lift or the five-stage ship lock. In order to improve the navigation efficiency and the utilization of passing facilities, a co-scheduling problem of ship lift and ship lock at the TGD is modeled in this paper. Besides, the improvement on effectiveness, safety and fairness of the navigation scheduling by introducing the ship lift is taken into account. The mathematical model proposed is, by nature, a two-stage problem, where the first-stage variables determine the assignments of passing facilities and the second-stage variables schedule the vessels. In order to solve the proposed mathematical model, a hybrid metaheuristic algorithm is used at the first stage to determine the allocation of vessels to different passing facilities and to different lockage through the maximization of facility utilization rate. The second-stage problem is then solved by CPLEX. The proposed mathematical model and algorithm are validated through a set of numerical experiments and a case study. The results show both the navigation efficiency and the utilization of passing facilities can be improved by using the proposed methods.

Synthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, also known as the incidence angle effect, impacts the segmentation of wide-swath images performed on absolute intensity values. We propose to integrate the target-specific intensity decay rates into a nonstationary statistical model, for use in a fully automatic and unsupervised segmentation algorithm. We demonstrate this concept by assuming Gaussian distributed log-intensities and linear decay rates, a fitting approximation for the smooth systematic decay observed for extended flat targets. The segmentation is performed on Sentinel-1, Radarsat-2, and UAVSAR wide-swath scenes containing open water, sea ice, and oil slicks. As a result, we obtain segments connected throughout the entire incidence angle range, thus overcoming the limitations of modeling that does not account for different per-target decays. The model simplicity also allows for short execution times and presents the segmentation approach as a potential operational algorithm. In addition, we estimate the log-linear decay rates and examine their potential for a physical interpretation of the segments.

This paper discusses the Inspiria charging station facility in Norway, which enables various charging point operators to offer different charging systems for different purposes and needs. The charging station can be considered a specific case of a shared economy, as users share the same infrastructure and much of the same space. By utilizing new technology and the differences in charging needs in an innovative way, the power requirements for charging can be controlled and the severity of high-load periods can be reduced—both within the charging station’s system and outside it. Using historical traffic data from the Inspiria charging station’s area and Monte Carlo simulations, this study investigated the impact of charging on the grid—both in the current period and in the future. Attention was paid to the impact associated with the usage of superfast chargers. The possibility of containing grid disturbances through utilization of local flexibility was investigated. Finally, we investigated the benefits that the charging station model brings to charging point operators and car owners. The research reported provides support for ambitions for accelerated roll-out and increased density of cost-effective charging points, the wider implication of which concerns the transition to fossil-free transport and the utilization of locally generated, renewable energy.

This paper presents a modeling approach for efficient simulation of slender structures, such as wires, cables and ropes. Lumped structural elements are connected using constraints. These are solved explicitly, using an elastic version of Baumgarte stabilization. This avoids singularities in the matrix inversions. The resulting explicit state-space formulation filters the higher order dynamics and can be solved using simple numerical integration methods. Constraints are demonstrated for modeling different aspects: Internal cable forces, one cable sliding along another cable and contact between cable and seabed. Also, a cable initialization routine is presented for rapid building of different interconnected cable geometries, ranging from cases in offshore crane operations to in-sea equipment such as seismic cables. Two case studies are presented to illustrate the effectiveness and the robustness of the proposed modeling approach; the first one being a test of two connected, sinking cables, and the last one being a larger case demonstrating the use of the cable library in an offshore seismic survey case.

Risk management is a fundamental approach to improving critical infrastructure systems’ safety against disruptive events. This approach focuses on designing robust critical infrastructure systems (CISs) that could resist disruptive events by minimizing the possible events’ probability and consequences using preventive and protective programs. However, recent disasters like COVID-19 have shown that most CISs cannot stand against all potential disruptions. Recently there is a transition from robust design to resilience design of CISs, increasing the focus on preparedness, response, and recovery. Resilient CISs withstand most of the internal and external shocks, and if they fail, they can bounce back to the operational phase as soon as possible using minimum resources. Moreover, in resilient CISs, early warning enables managers to get timely information about the proximity and development of distributions. An understanding of the concept of resilience, its influential factors, and available evaluation and analyzing tools are required to have effective resilience management. Moreover, it is important to highlight the current gaps. Technological resilience is a new concept associated with some ambiguity around its definition, its terms, and its applications. Hence, using the concept of resilience without understanding these variations may lead to ineffective pre- and post-disruption planning. A well-established systematic literature review can provide a deep understanding regarding the concept of resilience, its limitation, and applications. The aim of this paper is to conduct a systematic literature review to study the current research around technological CISs’ resilience. In the review, 192 primary studies published between 2003 and 2020 are reviewed. Based on the results, the concept of resilience has gradually found its place among researchers since 2003, and the number of related studies has grown significantly. It emerges from the review that a CIS can be considered as resilient if it has (i) the ability to imagine what to expect, (ii) the ability to protect and resist a disruption, (iii) the ability to absorb the adverse effects of disruption, (iv) the ability to adapt to new conditions and changes caused by disruption, and (v) the ability to recover the CIS’s normal performance level after a disruption. It was shown that robustness is the most frequent resilience contributing factor among the reviewed primary studies. Resilience analysis approaches can be classified into four main groups: empirical, simulation, index-based, and qualitative approaches. Simulation approaches, as dominant models, mostly study real case studies, while empirical methods, specifically those that are deterministic, are built based on many assumptions that are difficult to justify in many cases.

Chest computed tomography (CT) imaging has become indispensable for staging and managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies/abnormalities associated with COVID-19 has been performed majorly by the visual score. The development of automated methods for quantifying COVID-19 abnormalities in these CT images is invaluable to clinicians. The hallmark of COVID-19 in chest CT images is the presence of ground-glass opacities in the lung region, which are tedious to segment manually. We propose anamorphic depth embedding-based lightweight CNN, called Anam-Net, to segment anomalies in COVID-19 chest CT images. The proposed Anam-Net has 7.8 times fewer parameters compared to the state-of-the-art UNet (or its variants), making it lightweight capable of providing inferences in mobile or resource constraint (point-of-care) platforms. The results from chest CT images (test cases) across different experiments showed that the proposed method could provide good Dice similarity scores for abnormal and normal regions in the lung. We have benchmarked Anam-Net with other state-of-the-art architectures, such as ENet, LEDNet, UNet++, SegNet, Attention UNet, and DeepLabV3+. The proposed Anam-Net was also deployed on embedded systems, such as Raspberry Pi 4, NVIDIA Jetson Xavier, and mobile-based Android application (CovSeg) embedded with Anam-Net to demonstrate its suitability for point-of-care platforms. The generated codes, models, and the mobile application are available for enthusiastic users at https://github.com/NaveenPaluru/Segmentation-COVID-19 .

As the scaling of silicon PV cells and module manufacturing has driven solar energy penetration up and costs down, concentrator photovoltaic technologies, originally conceived as a cost-saving measure, have largely been left behind. The loss of market share by CPV is being locked in even as solar energy development encounters significant obstacles related to space constraints in many parts of the world. The inherently higher collection efficiency enabled by the use of concentrators could substantially alleviate these challenges, but the revival of CPV for this purpose requires substantial reinvention of the technology to actually capture the theoretically possible efficiency gains, and to do so at market-friendly costs. This article will discuss recent progress in key areas central to this reinvention, including miniaturization of cells and optics to produce compact, lightweight “micro-CPV” systems; hybridization of CPV with thermal, illumination and other applications to make use of unused energy streams such as diffuse light and waste heat; and the integration of sun-tracking into the CPV module architecture to enable greater light collection and more flexible deployment, including integration into built structures. Applications showing particular promise include thermal applications such as water heating, industrial processes and desalination; agricultural photovoltaics; building-integrated photovoltaics with dynamic daylighting capabilities; and chemical processes including photocatalysis and hydrogen production. By appropriately tailoring systems to the available solar resource and local energy demand, we demonstrate how CPV can finally achieve real-world efficiencies, or solar resource utilization factors, far higher than those of standard silicon-based PV systems. This makes the argument for sustained development of novel CPV designs that can be applied to the real-world settings where this efficiency boost will be most beneficial.

Abstract In this paper, solar photovoltaic hosting capacity within the electrical distribution network is estimated for different buses, and the impacts of high PV penetration are evaluated using power hardware-in-loop testing methods. It is observed that the considered operational constraints (i.e. voltage and loadings) and their operational limits have a significant impact on the hosting capacity results. However, with increasing photovoltaic penetration, some of the network buses reach maximum hosting capacity, which affects the network operation (e.g. bus voltages, line loading). The results show that even distributing the maximum hosting capacity among different buses can increase the bus voltage rise to 9%. To maintain the network bus voltages within acceptable limits, reactive power voltage-based droop control is implemented in the photovoltaic conditioning devices to test the dynamics of the network operation. The results show that implementation of the droop control technique can reduce the maximum voltage rise from 9% to 4% in the considered case. This paper also presents the impact of forming a mesh type network (i.e. from radial network) on the voltage profile during PV penetration, and a comparative analysis of the operational performance of a mesh type and radial type electrical network is performed. It is observed that the cumulative effect of forming a mesh type network along with a droop control strategy can further improve the voltage profile and contribute to increase photovoltaic penetration. The results are verified using an experimental setup of digital real-time simulator and power hardware-in-loop test methods. The results from this work will be useful for estimating the appropriate photovoltaic hosting capacity within a distribution network and implementation of a droop control strategy in power conditioning devices to maintain the network operational parameters within the specified limits. Highlights Voltage and line loading constraints’ combination can reduce PV hosting capacity by 50% as compared to only voltage as a constraint. Implementation of reactive power versus voltage droop control in PV power conditioning device can reduce voltage variation from 9% to 4%. In a PV integrated electrical energy network, line loading can be reduced by 20% if the network is configured from radial to mesh type.

It is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal set of image attributes which provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. Comprehensive studies have been focused on the analysis of relevant features for sea ice analysis obtained from different sensors, especially synthetic aperture radar. However, the outcomes of these studies are mostly data and application-dependent and can, therefore, rarely be generalized. In this article, we employ a feature selection method based on graph Laplacians, which is fully automatic and easy to implement. The proposed approach assesses relevant information on a global and local level using two metrics and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. In the recent study, we investigate the effectiveness of this approach for sea ice classification, using different multi-sensor data combinations. Experiments show the advantage of applying multi-sensor data sets and demonstrate that the attributes selected by our method result in high classification accuracies. We demonstrate that our approach automatically considers varying technical, sensor-specific, environmental, and sea ice conditions by employing flexible and adaptive feature selection method as a pre-processing step.

Training of Artificial Neural Networks (ANNs) have been improved over the years using meta heuristic algorithms that introduce randomness into the training method but they might be prone to falling into a local minima in a high-dimensional space and have low convergence rate with the iterative process. To cater for the inefficiencies of training such an ANN, a novel neural network is presented in this paper using the bio-inspired algorithm of the movement and mating of the mayflies. The proposed Mayfly algorithm is explored as a means to update weights and biases of the neural network. As compared to previous meta heuristic algorithms, the proposed approach finds the global minima cost at far less number of iterations and with higher accuracy. The network proposed, which is named Mayfly Algorithm based Neural Network (MFANN) consists of an input layer, a single hidden layer of 10 neurons and an output layer. Two University of California Irvine (UCI) database sample datasets have been used as benchmark for this study, namely the Banknote Authentication (BA) and the Cryotherapy, for which the training accuracy achieved is 99.2350% and 96.6102%, whereas the Testing accuracy is 99.1247% and 90% respectively. Comparative study with grey wolf optimization neural network (GWONN) and particle swarm optimization neural network (PSONN) reveal that the proposed MFANN achieves 1–2% better accuracy with training dataset and 2% better accuracy with testing dataset.

Autonomous surface vehicles are gaining increasing attention worldwide due to the potential benefits of improving safety and efficiency. This has raised the interest in developing methods for path planning that can reduce the risk of collisions, groundings, and stranding accidents at sea, as well as costs and time expenditure. In this paper, we review guidance, and more specifically, path planning algorithms of autonomous surface vehicles and their classification. In particular, we highlight vessel autonomy, regulatory framework, guidance, navigation and control components, advances in the industry, and previous reviews in the field. In addition, we analyse the terminology used in the literature and attempt to clarify ambiguities in commonly used terms related to path planning. Finally, we summarise and discuss our findings and highlight the potential need for new regulations for autonomous surface vehicles.

Radars with mmWave frequency modulated continuous wave (FMCW) technology accurately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple-input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including pedestrian, automotive, and unmanned aerial vehicle (UAV) (also known as drone). Peak, width, area, variance, and range are collected from range FFT profile peaks and fed into a machine learning model. In order to evaluate the performance, various light weight classification machine learning models such as logistic regression, Naive Bayes, support vector machine (SVM), and lightweight gradient boosting machine (GBM) are used. We demonstrate our findings by using outdoor measurements and achieve a classification accuracy of 95.6% by using LightGBM. The proposed method will be extremely useful in a wide range of applications, including cost-effective and dependable ground station traffic management and control systems for autonomous operations, and advanced driver-assistance systems (ADAS). The presented classification technique extends the potential of mmWave FMCW radar beyond the detection of range, velocity, and AoA to classification. mmWave FMCW radars will be more robust in computer vision, visual perception, and fully autonomous ground control and traffic management cyber-physical systems as a result of the added new feature.

Artificial intelligence is an enabling technology for autonomous surface vehicles, with methods such as evolutionary algorithms, artificial potential fields, fast marching methods, and many others becoming increasingly popular for solving problems such as path planning and collision avoidance. However, there currently is no unified way to evaluate the performance of different algorithms, for example with regard to safety or risk. This paper is a step in that direction and offers a comparative study of current state-of-the art path planning and collision avoidance algorithms for autonomous surface vehicles. Across 45 selected papers, we compare important performance properties of the proposed algorithms related to the vessel and the environment it is operating in. We also analyse how safety is incorporated, and what components constitute the objective function in these algorithms. Finally, we focus on comparing advantages and limitations of the 45 analysed papers. A key finding is the need for a unified platform for evaluating and comparing the performance of algorithms under a large set of possible real-world scenarios.

To achieve ultra-low energy consumption and decade-long battery lifetime for Internet of Things (IoT) networks, wake-up radio (WuR) appears as an eminent solution. While keeping devices in deep sleep for most of the time, a WuR enabled IoT device can be woken up for data transmission at any time by a wake-up call (WuC). However, collisions happen among WuCs for transmitter-initiated data reporting and among data packets for receiver-initiated data collection . In this article, we propose three novel hashing-based schemes in order to achieve collision-free data transmissions for receiver-initiated data collection. We consider first a simple scenario where all devices in a region of interest are reachable by a WuC message and propose a scheme which facilitates a scheduled time instant for data uploading of each device through a hash function. In the second scenario where IoT devices are distributed across a large region that cannot be covered by a single WuC, we propose two partitioning algorithms to enable data collection across multiple partitions. Furthermore, we extend the scenario by considering device mobility and propose another scheme which improves the partitioning algorithm to deal with mobility. Both analysis and simulations are performed to demonstrate the effectiveness of the proposed schemes.

In this article, we propose a novelteacher–student-based label propagation deep semisupervised learning (TSLP-SSL) method for sea ice classification based on Sentinel-1 synthetic aperture radar data. For sea ice classification, labeling the data precisely is very time consuming and requires expert knowledge. Our method efficiently learns sea ice characteristics from a limited number of labeled samples and a relatively large number of unlabeled samples. Therefore, our method addresses the key challenge of using a limited number of precisely labeled samples to achieve generalization capability by discovering the underlying sea ice characteristics also from unlabeled data. We perform experimental analysis considering a standard dataset consisting of properly labeled sea ice data spanning over different time slots of the year. Both qualitative and quantitative results obtained on this dataset show that our proposed TSLP-SSL method outperforms deep supervised and semisupervised reference methods.

Motion detection is vital for consumer electronics and the Internet of things (IOT). For a scenario where the motion is slow and gentle, the resolution of the motion sensor is critical for the detection, while the algorithm development is another critical issue to differentiate the motion signal from noise measurement. This paper investigates the feasibility of using higher order statistics kurtosis as a motion indicator. Statistical hypothesis test has been developed to assess the motion presence. Several experiments are conducted to test the feasibility and performance of the approach. The results show the approach is feasible, but with some limitations.

Source at https://ctsoc.ieee.org/ In this paper, we consider a wireless sensor network of nodes at the sea surface drifting due to wind and sea currents. In our scenario an Unmanned Aerial Vehicle (UAV) will be used to gather data from the sensor nodes. The goal is to find a flyable path which is optimal in terms of sensor node energy consumption, total channel throughput between the UAV and sensor nodes, flight time for the UAV and frequency of the node visits by the UAV. Finally, the path should also be optimal concerning node position estimation uncertainty. A Kalman Filter (KF) is used to estimate the nodes motions and Particle Swarm Optimization (PSO) is the method used to calculate the UAV path taking all of these objectives into account. The proposed node tracking aware path planning solution is compared to two other scenarios: One where the path planning is based on full knowledge of the node positions at all times, and one where path planning is based on the last known positions of the nodes.

Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of COVID-19 are heavy models that may not be easy to deploy in commonly utilized mobile platforms in point-of-care testing. In this work, we develop a lightweight mobile friendly efficient deep learning model for detection of COVID-19 using lung US images. Three different classes including COVID-19, pneumonia, and healthy were included in this task. The developed network, named as Mini-COVIDNet, was bench-marked with other lightweight neural network models along with state-of-the-art heavy model. It was shown that the proposed network can achieve the highest accuracy of 83.2% and requires a training time of only 24 min. The proposed Mini-COVIDNet has 4.39 times less number of parameters in the network compared to its next best performing network and requires a memory of only 51.29 MB, making the point-of-care detection of COVID-19 using lung US imaging plausible on a mobile platform. Deployment of these lightweight networks on embedded platforms shows that the proposed Mini-COVIDNet is highly versatile and provides optimal performance in terms of being accurate as well as having latency in the same order as other lightweight networks. The developed lightweight models are available at https://github.com/navchetan-awasthi/Mini-COVIDNet.

Unscheduled power disturbances cause severe consequences both for customers and grid operators. To defend against such events, it is necessary to identify the causes of interruptions in the power distribution network. In this work, we focus on the power grid of a Norwegian community in the Arctic that experiences several faults whose sources are unknown. First, we construct a data set consisting of relevant meteorological data and information about the current power quality logged by power-quality meters. Then, we adopt machine-learning techniques to predict the occurrence of faults. Experimental results show that both linear and non-linear classifiers achieve good classification performance. This indicates that the considered power quality and weather variables explain well the power disturbances. Interpreting the decision process of the classifiers provides valuable insights to understand the main causes of disturbances. Traditional features selection methods can only indicate which are the variables that, on average, mostly explain the fault occurrences in the dataset. Besides providing such a global interpretation, it is also important to identify the specific set of variables that explain each individual fault. To address this challenge, we adopt a recent technique to interpret the decision process of a deep learning model, called Integrated Gradients. The proposed approach allows gaining detailed insights on the occurrence of a specific fault, which are valuable for the distribution system operators to implement strategies to prevent and mitigate power disturbances. publishedVersion

The time required for rescue is a critical factor for surviving a marine incident. The regulatory framework, International Maritime Organization (IMO) Polar Code, utilizes a risk-based approach. It states that the vessel operators are to define the time required for rescue but never less than 5 days. Based on experience from the classification society DNV GL, utilization of the minimum requirement of five days is the current industry standard when conducting risk assessments. The dimensioning of search and rescue resources is a national issue. There are no international requirements defining the adequacy of the resources for different geographical areas. The remoteness and lack of resources present within the IMO Polar Code area imposes a significant challenge for mariners in distress. The time required for rescue is highly dependent on multiple variables. Based on this study, the number of persons to be rescued, the number and type of evacuation platforms and the distance each evacuation platform must travel significantly impacts the time required for rescue. In addition, the meteorological and oceanographical (metocean) conditions play a significant role when determining the efficiency of a search and rescue operation.

In this manuscript we evaluate the potential of photovoltaic systems to meet some dedicated energy demand in specific geographic locations. Our approach is based on location-specific constraints rather than on pre-established, location-independent methodologies or assumptions. First, we propose that a thorough analysis of the socio-economic and technical possibilities of a location must act as the guide to optimize the deployment of renewables. This requires detailed knowledge of the area. Second, we propose that optimizing the exploitation of renewables by focusing on a particular location can also lead to successful outcomes with global impact. With this in mind we focus our attention on the Arctic region, known for its highly seasonal solar availability, and the challenge posed by increasing cruise ship tourism and corresponding air pollution. Our study targets Tromso city, Norway, and we show that solar energy generation could be a strong contribution for charging cruise ships in the summer with no need for generation and transmission investments. Our study opens the door to shifting to a location specific paradigm to seek sustainable energy solutions with the possibility to have a global impact.

While designing the power requirements of a ship, the most important factor to be considered is the ship resistance, or the sea drag forces acting on the ship. It is important to have an estimate of the ship resistance while designing the propulsion system since the power required to overcome the sea drag forces contribute to ‘losses’ in the propulsion system. There are three main methods to calculate ship resistance: Statistical methods like the Holtrop-Mennen (HM) method, numerical analysis or CFD (Computational Fluid Dynamics) simulations, and model testing, i.e. scaled model tests in towing tanks. At the start of the design stage, when only basic ship parameters are available, only statistical models like the HM method can be used. Numerical analysis/ CFD simulations and model tests can be performed only when the complete 3D design of the ship is completed. The present paper aims at predicting the calm water ship resistance using CFD simulations, using the Flow-3D software package. A case study of a roll-on/roll-off passenger (RoPax) ferry was investigated. Ship resistance was calculated at various ship speeds. Since the mesh affects the results in any CFD simulation, multiple meshes were used to check the mesh sensitivity. The results from the simulations were compared with the estimate from the HM method. The results from simulations agreed well with the HM method for low ship speeds. The difference in the results was considerably high compared to the HM method for higher ship speeds. The capability of Flow-3D to perform ship resistance analysis was demonstrated.

Track geometry is a fundamental subject in railway construction. With the demand for increased capacity in terms of load and speed, the need for suitable transitions between consecutive track sections is highly relevant. Properly constructed transition curves lead to improved travel comfort, increased safety, and reduced wear. The well known clothoid curve is widely used as a transition curve, however, the linear curvature is not sufficiently smooth to meet the requirements for railways carrying high speed trains or heavy hauls. Blending spline curves are flexible spline constructions possessing favourable smoothness properties at the end points, which makes them considerable for use as transition curves. This paper demonstrates some selected blending splines applied as transition curves between two existing circular arc segments selected from the Ofotbanen railway. The main results in this paper are related to the smoothness at the end points and the behaviour of the curvature of the curves, where the new transition curves were shown to be smoother than the original clothoid. Another new result is the observation that the proposed method allows for the improvement of existing railways without forcing extensive changes to the original track. Some representative examples are included to highlight the flexibility of this first instance of blending splines as transition curves.

Smart phones and sensor technology represent a key part of everyday life and are being used in areas such as safety, training, healthcare and others. Utilizing an array of internal sensors and a metal detector requires an evaluation of the precision of the measurements and performance reviews. Metal detectors are versatile, with uses in healthcare as well as recreational, but a common issue often seen in the proprietary equipment is bad presentation of data. Usually the user interface is just numbers on a display, simplified graphs or sounds. By combining smartphone sensors with a metal detector and a custom mount we model a mapping between the virtual and physical model, a digital twin. In this paper we are utilizing the computing capabilities of a smartphone and employing visualization techniques not possible by partial information. In addition, we present an improved graphical user interface without any proprietary accessories. For this purpose, preliminary case studies are included as a part of a prototype in development.

Convolutional neural networks (CNNs) have been widely applied in hyperspectral imagery (HSI) classification. However, their classification performance might be limited by the scarcity of labeled data to be used for training and validation. In this paper, we propose a novel lightweight shuffled group convolutional neural network (abbreviated as SG-CNN) to achieve efficient training with a limited training dataset in HSI classification. SG-CNN consists of SG conv units that employ conventional and atrous convolution in different groups, followed by channel shuffle operation and shortcut connection. In this way, SG-CNNs have less trainable parameters, whilst they can still be accurately and efficiently trained with fewer labeled samples. Transfer learning between different HSI datasets is also applied on the SG-CNN to further improve the classification accuracy. To evaluate the effectiveness of SG-CNNs for HSI classification, experiments have been conducted on three public HSI datasets pretrained on HSIs from different sensors. SG-CNNs with different levels of complexity were tested, and their classification results were compared with fine-tuned ShuffleNet2, ResNeXt, and their original counterparts. The experimental results demonstrate that SG-CNNs can achieve competitive classification performance when the amount of labeled data for training is poor, as well as efficiently providing satisfying classification results.

Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose a new bio-inspired learning model for a single-class classifier to detect abnormality in video object trajectories. The method uses a simple but dynamic extreme learning machine (ELM) and hierarchical temporal memory (HTM) together referred to as ELM-HTM in an unsupervised way to learn and classify time series patterns. The method has been tested on trajectory sequences in traffic surveillance to find abnormal behaviors such as high-speed, unusual stops, driving in wrong directions, loitering, etc. Experiments have also been performed with 3D air signatures captured using sensors and used for biometric authentication(forged/genuine). The results indicate a significant gain over training time and classification accuracy. The proposed method outperforms in predicting long-time patterns by observing small steps with an average accuracy gain of 15% as compared to the state-of-the-art HTM. The method has applications in detecting abnormal activities in videos by learning the movement patterns as well as in biometric authentication.

Automated feature extraction from program source-code such that proper computing resources could be allocated to the program is very difficult given the current state of technology. Therefore, conventional methods call for skilled human intervention in order to achieve the task of feature extraction from programs. This research is the first to propose a novel human-inspired approach to automatically convert program source-codes to visual images. The images could be then utilized for automated classification by visual convolutional neural network (CNN) based algorithm. Experimental results show high prediction accuracy in classifying the types of program in a completely automated manner using this approach.

Fringe patterns encode the information about the result of a measurement performed via widely used optical full-field testing methods, e.g., interferometry, digital holographic microscopy, moiré techniques, structured illumination etc. Affected by the optical setup, changing environment and the sample itself fringe patterns are often corrupted with substantial noise, strong and uneven background illumination and exhibit low contrast. Fringe pattern enhancement, i.e., noise minimization and background term removal, at the pre-processing stage prior to the phase map calculation (for the measurement result decoding) is therefore essential to minimize the jeopardizing effect the mentioned error sources have on the optical measurement outcome. In this contribution we propose an automatic, robust and highly effective fringe pattern enhancement method based on the novel period-guided bidimensional empirical mode decomposition algorithm (PG-BEMD). The spatial distribution of the fringe period is estimated using the novel windowed approach and then serves as an indicator for the truly adaptive decomposition with the filter size locally adjusted to the fringe pattern density. In this way the fringe term is successfully extracted in a single (first) decomposition component alleviating the cumbersome mode mixing phenomenon and greatly simplifying the automatic signal reconstruction. Hence, the fringe term is dissected without the need for modes selection nor summation. The noise removal robustness is ensured employing the block matching 3D filtering of the fringe pattern prior to its decomposition. Performance validation against previously reported modified empirical mode decomposition techniques is provided using numerical simulations and experimental data verifying the versatility and effectiveness of the proposed approach.

The resilience of a system can be considered as a function of its reliability and recoverability. Hence, for effective resilience management, the reliability and recoverability of all components which build up the system need to be identified. After that, their importance should be identified using an appropriate model for future resource allocation. The critical infrastructures are under dynamic stress due to operational conditions. Such stress can significantly affect the recoverability and reliability of a system’s components, the system configuration, and consequently, the importance of components. Hence, their effect on the developed importance measure needs to be identified and then quantified appropriately. The dynamic operational condition can be modeled using the risk factors. However, in most of the available importance measures, the effect of risk factors has not been addressed properly. In this paper, a reliability importance measure has been used to determine the critical components considering the effect of risk factors. The application of the model has been shown through a case study.

Accurate wind power forecasting plays a critical role in the operation of wind parks and the dispatch of wind energy into the power grid. With excellent automatic pattern recognition and nonlinear mapping ability for big data, deep learning is increasingly employed in wind power forecasting. However, salient realities are that in-situ measured wind data are relatively expensive and inaccessible and correlation between steps is omitted in most multistep wind power forecasts. This paper is the first time that data augmentation is applied to wind power forecasting by systematically summarizing and proposing both physics-oriented and data-oriented time-series wind data augmentation approaches to considerably enlarge primary datasets, and develops deep encoder-decoder long short-term memory networks that enable sequential input and sequential output for wind power forecasting. The proposed augmentation techniques and forecasting algorithm are deployed on five turbines with diverse topographies in an Arctic wind park, and the outcomes are evaluated against benchmark models and different augmentations. The main findings reveal that on one side, the average improvement in RMSE of the proposed forecasting model over the benchmarks is 33.89%, 10.60%, 7.12%, and 4.27% before data augmentations, and increases to 40.63%, 17.67%, 11.74%, and 7.06%, respectively, after augmentations. The other side unveils that the effect of data augmentations on prediction is intricately varying, but for the proposed model with and without augmentations, all augmentation approaches boost the model outperformance from 7.87% to 13.36% in RMSE, 5.24% to 8.97% in MAE, and similarly over 12% in QR90. Finally, data-oriented augmentations, in general, are slightly better than physics-driven ones.

Road conditions during the winter months in Nordic countries can be highly unstable. Slippery roads combined with heavy haul traffic and ordinary road users can create dangerous, even lethal, situations if road maintenance is unsuccessful. Accidents and critical road conditions may lead to blocked roads, putting strain on a limited number of main roads in many regions, and may in the worst case isolate areas entirely. Using sensors in winter road assessment has been a popular topic for over 20 years. However, with today&rsquo, s developments connected to smaller and cheaper sensors, new opportunities are presenting themselves. In this study, we performed preliminary experiments on a variety of sensors, both commercial and experimental, to evaluate their benefits in possible hybrid sensor technology, which can give a more complete characterization of the road surface than what is possible from just one sensor. From the collected data and visual analysis of the results, the idea of a hybrid sensor seems promising when considering the differences in the tested sensors and how they may complement each other.

Special issue 2019 Selected Papers from the IMEKO TC-19 International Workshop on Metrology for the Sea.-- 17 pages, 9 figures, 2 tables, supplementary material http://www.mdpi.com/1424-8220/20/6/1614/s1, This paper presents the technological developments and the policy contexts for the project “Autonomous Robotic Sea-Floor Infrastructure for Bentho-Pelagic Monitoring” (ARIM). The development is based on the national experience with robotic component technologies that are combined and merged into a new product for autonomous and integrated ecological deep-sea monitoring. Traditional monitoring is often vessel-based and thus resource demanding. It is economically unviable to fulfill the current policy for ecosystem monitoring with traditional approaches. Thus, this project developed platforms for bentho-pelagic monitoring using an arrangement of crawler and stationary platforms at the Lofoten-Vesterålen (LoVe) observatory network (Norway). Visual and acoustic imaging along with standard oceanographic sensors have been combined to support advanced and continuous spatial-temporal monitoring near cold water coral mounds. Just as important is the automatic processing techniques under development that have been implemented to allow species (or categories of species) quantification (i.e., tracking and classification). At the same time, real-time outboard processed three-dimensional (3D) laser scanning has been implemented to increase mission autonomy capability, delivering quantifiable information on habitat features (i.e., for seascape approaches). The first version of platform autonomy has already been tested under controlled conditions with a tethered crawler exploring the vicinity of a cabled stationary instrumented garage. Our vision is that elimination of the tether in combination with inductive battery recharge trough fuel cell technology will facilitate self-sustained long-term autonomous operations over large areas, serving not only the needs of science, but also sub-sea industries like subsea oil and gas, and mining, This project is funded by The Norwegian Research Council, Federal Ministry for Economic Affairs and Energy of Germany (03SX464C) and the HGF-project MOSES, Spanish Centre for the Development of Industrial Technology (EXP 00108707/SERA-20181020), and co-funded by European Union’s Horizon 2020 research and innovation program under the framework of ERA-NET Cofund MarTERA (Maritime and Marine Technologies for a new Era), With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful classification. In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. GB detection is a challenging application, showing high heterogeneity in the cellular morphology across different patients. We employed an HSI microscope, with a spectral range from 400 to 1000 nm, to collect 517 HS cubes from 13 GB patients using 20× magnification. Using a convolutional neural network (CNN), we were able to automatically detect GB within the pathological slides, achieving average sensitivity and specificity values of 88% and 77%, respectively, representing an improvement of 7% and 8% respectively, as compared to the results obtained using RGB (red, green, and blue) images. This study demonstrates that the combination of hyperspectral microscopic imaging and deep learning is a promising tool for future computational pathologies.

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. A main challenge in the unsupervised problem setting is to avoid that change pixels affect the learning of the translation function. We propose two new network architectures trained with loss functions weighted by priors that reduce the impact of change pixels on the learning objective. The change prior is derived in an unsupervised fashion from relational pixel information captured by domain-specific affinity matrices. Specifically, we use the vertex degrees associated with an absolute affinity difference matrix and demonstrate their utility in combination with cycle consistency and adversarial training. The proposed neural networks are compared with the state-of-the-art algorithms. Experiments conducted on three real data sets show the effectiveness of our methodology.

Accurate detection of accelerometer non-wear time is crucial for calculating physical activity summary statistics. In this study, we evaluated three epoch-based non-wear algorithms (Hecht, Troiano, and Choi) and one raw-based algorithm (Hees). In addition, we performed a sensitivity analysis to provide insight into the relationship between the algorithms’ hyperparameters and classification performance, as well as to generate tuned hyperparameter values to better detect episodes of wear and non-wear time. We used machine learning to construct a gold-standard dataset by combining two accelerometers and electrocardiogram recordings. The Hecht and Troiano algorithms achieved poor classification performance, while Choi exhibited moderate performance. Meanwhile, Hees outperformed all epoch-based algorithms. The sensitivity analysis and hyperparameter tuning revealed that all algorithms were able to achieve increased classification performance by employing larger intervals and windows, while more stringently defining artificial movement. These classification gains were associated with the ability to lower the false positives (type I error) and do not necessarily indicate a more accurate detection of the total non-wear time. Moreover, our results indicate that with tuned hyperparameters, epoch-based non-wear algorithms are able to perform just as well as raw-based non-wear algorithms with respect to their ability to correctly detect true wear and non-wear episodes.

This paper presents a techno-economic optimisation tool to study how the power system expansion decisions can be taken in a more economical and efficient way, by minimising the consequent costs of network reinforcement and reconfiguration. Analyses are performed to investigate how the network reinforcement and reconfiguration should be planned, within a time horizon of several years, by continuously keeping the network feasibility and ability to satisfy the load. The main contribution of this study is the inclusion of key features within the mathematical model to enhance the investment decision making process. A representative maintenance cost of existing cables and apparatus is included, to analyse the influence of the historical performance of the electric items on the investment decisions. A multihorizon methodology is developed to take into account the long term variation of the demand, combined with the long term variation of cables maintenance costs. Moreover, technological learning coefficients are considered, to take into account the investment costs reductions that arise when an investment in network restructuring and/or reconfiguration is repeated throughout the years. Finally, construction time constraints are included to find a proper investment scheduling that allows a feasible power flow, also during the years required to build a new connection or restructure an existing one. This study is also providing recommendations for future research directions within the power system reliability field. The analyses show the important and urgent need of proper methodologies for a better definition of cables projected maintenance costs and learning coefficients dedicated to network restructuring, reconfiguration and expansion., Comment: 22 pages, 21 figures

Tumor segmentation is a crucial but difficult task in treatment planning and follow-up of cancerous patients. The challenge of automating the tumor segmentation has recently received a lot of attention, but the potential of utilizing hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI), a novel and promising imaging modality in oncology, is still under-explored. Recent approaches have either relied on manual user input and/or performed the segmentation patient-by-patient, whereas a fully unsupervised segmentation framework that exploits the available information from all patients is still lacking. We present an unsupervised across-patients supervoxel-based clustering framework for lung tumor segmentation in hybrid PET/MRI. The method consists of two steps: First, each patient is represented by a set of PET/MRI supervoxel-features. Then the data points from all patients are transformed and clustered on a population level into tumor and non-tumor supervoxels. The proposed framework is tested on the scans of 18 non-small cell lung cancer patients with a total of 19 tumors and evaluated with respect to manual delineations provided by clinicians. Experiments study the performance of several commonly used clustering algorithms within the framework and provide analysis of (i) the effect of tumor size, (ii) the segmentation errors, (iii) the benefit of across-patient clustering, and (iv) the noise robustness. The proposed framework detected 15 out of 19 tumors in an unsupervised manner. Moreover, performance increased considerably by segmenting across patients, with the mean dice score increasing from (patient-by-patient) to (across-patients). Results demonstrate that both spectral clustering and Manhattan hierarchical clustering have the potential to segment tumors in PET/MRI with a low number of missed tumors and a low number of false-positives, but that spectral clustering seems to be more robust to noise. /© 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Winter road assessment is a research field with considerable progress over the last 10 years. Various sensors and methods have been tested and analysed, often in a laboratory setting, in order to come up with robust and valid assessment tools that can be used to warn the driver, road users in general, and maintenance personnel of critical conditions. In this paper we compare the field measurements of an RCM411 and a MARWIS sensor with each other and with previously performed laboratory experiments, we reflect on OBD-II as a tool in winter road assessment, and perform initial field tests with an experimental radar sensor. The results of the RCM411/MARWIS comparison shows significant correlation between our field experiments and the laboratory experiments, OBD-II appears to be fitting as a supplementary tool in the assessments, and the experimental radar tests uncovers a need for more investigation.

The United Nations’ goal of generating sustainable industry, innovation, and infrastructure is the point of departure for our reflective paper. The paper elaborates on the concepts of digital infrastructure, service-oriented architecture, and microservices. It emphasizes the benefits and challenges of creating a sustainable infrastructure based on a service-oriented environment, in which cloud services constitute an important part. We outline the prerequisites for obtaining a sustainable digital infrastructure based on services. Service-oriented architecture (SOA) and recently, microservice architecture, and cloud services, can provide organizations with the improved agility and flexibility essential for generating sustainability in a market focusing on digitalization. The reuse capability of SOA provides a common pool of information technology (IT) resources and qualifies as a green IT approach that impacts environmental protection. Previous research has identified IT and business alignment together with SOA governance as the most critical criteria when implementing SOA. This paper discusses these issues in-depth to explain sustainability.

Standalone photovoltaic system (SPVS) is usually embedded with an energy storage unit to overcome the intermittency of photovoltaic (PV) generation as well as to address load variations in off-grid operation. In SPVS energy systems, batteries can serve as the long term energy storage and contributing to the large portion of the energy demand but to overcome the load intermittency, it necessitates a fast response energy storage embedded with the battery as a hybrid energy storage (HES) for dynamic loads (e.g., Electric Vehicle loads, emergency power management). In this work, Lead-Acid (LA) battery and supper capacitor (SC) array are used as the HES. HES helps not only in increasing more utilization of PV generated power but also in improving system dynamics and stability for power intensive loads operation. This paper presents innovative power management and control strategies for a HES connected in DC coupled architecture. The presented architecture can also be used for DC micro grids. Power management strategies are developed in a hierarchical architecture as an event driven finite state machine. Primary control is implemented using current mode control and voltage mode control adapted at the bi-directional DC-DC converters and the voltage source inverter connected to the HES, respectively. The performance of the developed power management strategies for the HES is analyzed for typical load dynamics.

This paper proposes a novel human-inspired methodology called IRON-MAN ( I ntegrated R ati ON al prediction and M otionless AN alysis ) for mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video ( TMAV ). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image ( ECTI ) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption and lifespan reliability of the device.

Diagrammatic reasoning (DR) problems are well known. However, solving DR problems represented in 4 × 1 Raven’s Progressive Matrix (RPM) form using computer vision and pattern recognition has not yet been tried. Emergence of deep learning techniques aided by advanced computing can be exploited to solve such DR problems. In this paper, we propose a new learning framework by combining LSTM and Convolutional LSTM to solve 4 × 1 DR problems. Initially, the elementary geometrical shapes in such problems are detected using a typical CNN-based detector. Next, relations of various shapes are analyzed and a high-level feature set is produced and processed in the LSTM framework. A new 4 × 1 DR dataset has been prepared and made available to the research community. We believe, it will be helpful in advancing this research further. We have compared our method with some of the existing frameworks that can be used for solving RPM-guided DR problems. We have recorded 18–20% increase in the average prediction accuracy as compared to the prior frameworks when applied to RPM-guided DR problems. We believe the CV research community will be interested to carry out similar research, particularly to investigate the feasibility of solving other types of known DR problems.