Micro Aerial Vehicles (MAVs) are platforms that received great attention during the last decade. Recently, the mining industry has been considering the usage of aerial autonomous platforms in their processes. This article initially investigates potential application scenarios for this technology in mining. Moreover, one of the main tasks refer to surveillance and maintenance of infrastructure assets. Employing these robots for underground surveillance processes of areas like shafts, tunnels or large voids after blasting, requires among others the development of elaborate navigation modules. This paper proposes a method to assist the navigation capabilities of MAVs in challenging mine environments, like tunnels and vertical shafts. The proposed method considers the use of Potential Fields method, tailored to implement a sense-and-avoid system using a minimal ultrasound-based sensory system. Simulation results demonstrate the effectiveness of the proposed strategy.

Unmanned Aerial Vehicles (UAVs) equipped with visual sensors are widely used in area coverage missions. Guaranteeing full coverage coupled with camera footprint is one of the most challenging tasks, thus, in the presented novel approach a coverage path planner for the inspection of 2D areas is established, a 3 Degree of Freedom (DoF) camera movement is considered and the shortest path from the taking off to the landing station is generated, while covering the target area. The proposed scheme requires a priori information about the boundaries of the target area and generates the paths in an offline process. The efficacy and the overall performance of the proposed method has been experimentally evaluated in multiple indoor inspection experiments with convex and non convex areas. Furthermore, the image streams collected during the coverage tasks were post-processed using image stitching for obtaining a single overview of the covered scene.

The ever-increasing number of electronic content stored in digital libraries requires a significant amount of effort in cataloguing and has led to self-deposit solutions where the authors submit and publish their own digital records. Even in self-deposit, going through the abstract and assigning subject terms or keywords is a time consuming and expensive process, yet crucial for the metadata quality of the record that affects retrieval. Therefore, an automatic, or even a semi-automatic process that can recommend topics for a new entry is of huge practical value. A system that can address that has to rely basically on two components, one component for efficiently representing the relevant information of the new document and one component for recommending an appropriate set of topics based on the representation of the previous stage. In this work, different candidate solutions for both components are investigated and compared. For the first stage both distributed Document to Vector (doc2vec) and conventional Bag of Words (BoW) components are employed, while for the latter two different transformation approaches from the field of multi-label classification are compared. For the comparison, a collection of Ph.D. abstracts (~19000 documents) from the MIT Libraries Dspace repository is used suggesting that different combinations can provide high quality solutions.

Assessment of sensorimotor function is crucial during the rehabilitation process of various physical disorders, including impairments of the hand. While moment performance can be accurately assessed in movement science laboratories involving highly specialized personnel and facilities there is a lack of feasible objective methods for the general clinic. This paper describes a novel approach to sensorimotor assessment using an intuitive test and a specifically tailored image processing pipeline for the quantification of the test. More specifically the test relies on the patient being instructed on following a zig-zag pattern using a handled laser pointer. The movement of the pointer is tracked using image processing algorithm capable of automating the whole procedure. The method has potential for feasible objective clinical assessment of the hand and other body parts

In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault, using the power-set approach. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity. For the feature extraction stage, the time-frequency representation, resulting from the application of the short time Fourier transform of the start-up current is exploited. The proposed approach is validated using simulation data with promising results.

Background

Sensorimotor disturbances of the hand such as altered neuromuscular control and reduced proprioception have been reported for various musculoskeletal disorders. This can have major impact on daily activities such as dressing, cooking and manual work, especially when involving high demands on precision and therefore needs to be considered in the assessment and rehabilitation of hand disorders. There is however a lack of feasible and accurate objective methods for the assessment of movement behavior, including proprioception tests, of the hand in the clinic today. The objective of this observational cross- sectional study was to develop and conduct preliminary validation testing of a new method for clinical assessment of movement sense of the wrist using a laser pointer and an automatic scoring system of test results.

Methods

Fifty physiotherapists performed a tracking task with a hand-held laser pointer by following a zig-zag pattern as accurately as possible. The task was performed with left and right hand in both left and right directions, with three trials for each hand movement. Each trial was video recorded and analysed with a specifically tailored image processing pipeline for automatic quantification of the test. The main outcome variable was Acuity, calculated as the percent of the time the laser dot was on the target line during the trial.

Results

The results showed a significantly better Acuity for the dominant compared to non-dominant hand. Participants with right hand pain within the last 12 months had a significantly reduced acuity (p < 0.05), and although not significant there was also a similar trend for reduced Acuity also for participants with left hand pain. Furthermore, there was a clear negative correlation between Acuity and Speed indicating a speed-accuracy trade off commonly found in manual tasks. The repeatability of the test showed acceptable intra class correlation (ICC_2.1) values (0.68-0.81) and standard error of measurement values ranging between 5.0–6.3 for Acuity.

Conclusions

The initial results suggest that the test may be a valid and feasible test for assessment of the movement sense of the hand. Future research should include assessments on different patient groups and reliability evaluations over time and between testers.

Evaluation of cardiotocogram (CTG) is a standard approach employed during pregnancy and delivery. But, its interpretation requires high level expertise to decide whether the recording is Normal, Suspicious or Pathological. Therefore, a number of attempts have been carried out over the past three decades for development automated sophisticated systems. These systems are usually (multiclass) classification systems that assign a category to the respective CTG. However most of these systems usually do not take into consideration the natural ordering of the categories associated with CTG recordings. In this work, an algorithm that explicitly takes into consideration the ordering of CTG categories, based on binary decomposition method, is investigated. Achieved results, using as a base classifier the C4.5 decision tree classifier, prove that the ordinal classification approach is marginally better than the traditional multiclass classification approach, which utilizes the standard C4.5 algorithm for several performance criteria.

The aim of this article is to present an example of a novel cloud computing infrastructure for big data analytics in the Process Control Industry. Latest innovations in the field of Process Analyzer Techniques (PAT), big data and wireless technologies have created a new environment in which almost all stages of the industrial process can be recorded and utilized, not only for safety, but also for real time optimization. Based on analysis of historical sensor data, machine learning based optimization models can be developed and deployed in real time closed control loops. However, still the local implementation of those systems requires a huge investment in hardware and software, as a direct result of the big data nature of sensors data being recorded continuously. The current technological advancements in cloud computing for big data processing, open new opportunities for the industry, while acting as an enabler for a significant reduction in costs, making the technology available to plants of all sizes. The main contribution of this article stems from the presentation for a fist time ever of a pilot cloud based architecture for the application of a data driven modeling and optimal control configuration for the field of Process Control. As it will be presented, these developments have been carried in close relationship with the process industry and pave a way for a generalized application of the cloud based approaches, towards the future of Industry 4.0

The steel industry involves energy-intensive processessuch as combustion processes whose accurate modellingvia first principles is both challenging and unlikely to leadto accurate models let alone cast time-varying dynamics anddescribe the inevitable wear and tear. In this paper we addressthe main objective which is the reduction of energy consumptionand emissions along with the enhancement of the autonomy ofthe controlled process by online modelling and uncertaintyawarepredictive control. We propose a risk-sensitive modelselection procedure which makes use of the modern theoryof risk measures and obtain dynamical models using processdata from our experimental setting: a walking beam furnaceat Swerea MEFOS. We use a scenario-based model predictivecontroller to track given temperature references at the threeheating zones of the furnace and we train a classifier whichpredicts possible drops in the excess of Oxygen in each heatingzone below acceptable levels. This information is then used torecalibrate the controller in order to maintain a high qualityof combustion, therefore, higher thermal efficiency and loweremissions