Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Computer Vision based Robust Approach for Joggled Fish Plate Detection in Drone Images

Periodic inspections of the railroad tracks and their components are necessary to ensure safe transportation. Manual inspections involve the railroad personnel to either inspect the tracks by walk or ride a hi-rail vehicle at comparable speeds. However, manual operations cannot be carried out regularly in remote areas or terrorist affected areas. The unmanned vehicles such as drones are best suited in these cases. In addition, automated computer vision based approaches lead to more accurate as well as frequent condition monitoring of components. The joggled fish plates are track components which provide with emergency repairs to fractured rail sections while maintaining the steadiness and the continuity of the train. Their monitoring is essential for smooth passage of trains. Therefore, in this paper, we have described a novel template matching based approach for detection of joggled fish plates in the drone imagery. Image based statistics and Non-maximum suppression techniques have also been used to reduce the false detections. The specificity or the detection accuracy value achieved is 95%. The proposed approach is robust to noise and works well under various lighting and environmental conditions.
A New Hybrid Submodule for MMC with DC Fault Ride-Through Capability

Modular multilevel converter (MMC) has become the most prominent topology for high voltage direct current (HVDC) applications due to its modularity, lower losses, less filtering requirements and flexible controllability. However, MMC still lacks in terms of efficiency and fault tolerant capability compared to the line commutated converters (LCC) based HVDC systems. This paper proposes a thyristor bypassed fault tolerant half bridge submodule (TBFT-HBSM) for MMC to achieve high efficiency as that in LCC while retaining DC fault tolerant capability and controllability. The lower switch of conventional HBSMs is replaced by anti-parallel thyristor switches to reduce the overall conduction losses of the converter. A new control algorithm has been proposed to balance the arm voltages. Moreover, loss analysis of proposed submodule (SM) is carried out and its efficiency compared with existing SM topologies. Simulation as well as experimental studies are carried out to verify the effectiveness of the proposed SM structure and its control.
Online Contextual Influence Maximization With Costly Observations

In the online contextual influence maximization problem with costly observations, the learner faces a series of epochs in each of which a different influence spread process takes place over a network. At the beginning of each epoch, the learner exogenously influences (activates) a set of seed nodes in the network. Then, the influence spread process takes place over the network, through which other nodes get influenced. The learner has the option to observe the spread of influence by paying an observation cost. The goal of the learner is to maximize its cumulative reward, which is defined as the expected total number of influenced nodes over all epochs minus the observation costs. We depart from the prior work in three aspects: 1) the learner does not know how the influence spreads over the network, i.e., it is unaware of the influence probabilities; 2) influence probabilities depend on the context; and 3) observing influence is costly. We consider two different influence observation settings: costly edge-level feedback, in which the learner freely observes the set of influenced nodes, but pays to observe the influence outcomes on the edges of the network; and costly node-level feedback, in which the learner pays to observe whether a node is influenced or not. Since the offline influence maximization problem itself is NP-hard, for these settings, we develop online learning algorithms that use an approximation algorithm as a subroutine to obtain the set of seed nodes in each epoch. When the influence probabilities are Hölder continuous functions of the context, we prove that these algorithms achieve sublinear regret (for any sequence of contexts) with respect to an approximation oracle that knows the influence probabilities for all contexts. Our numerical results on several networks illustrate that the proposed algorithms perform on par with the state-of-the-art methods even when the observations are cost free.
Minimization of the Thermal Stress on the Rotor Side Converter of DFIG while Operating Around Synchronous Speed

For power electronic converters-based variable speed doubly-fed-induction-generator (DFIG), the operation near the synchronous speed (usually a very low slip) can cause the rotor side converter to become significantly stressed due to the high temperature fluctuations which can lead to a reduced reliability over time. In this paper, the semiconductor junction temperatures and power losses have been quantified. The thermal stress can be reduced by decreasing the switching frequency. But a low switching frequency can cause harmonics and torsional vibration. A switching control technique is proposed in this paper to deal with the tradeoff between the pulse width modulation (PWM) harmonics and semiconductor junction temperature. A reference point is determined so that the parameters e.g. the junction temperature and the total harmonic distortion (THD) follows this reference point. In this paper, limited reactive power support is considered to reduce the rotor current amplitude and improve the temperature fluctuations is by limiting reactive power support from the DFIG. This paper also presents a coordinated control scheme with switching control and rotor current derating to effectively reduce the conductor thermal stress without compromising its performance reliability.
Prototype Non-Volatile FPGA SVPWM Controller for 3-Level Inverter

This paper describes the development of a prototype non-volatile FPGA space vector pulse width modulated digital controller for 3-level inverter. Algorithm for digital controller is first realized using simulink blocks from HDL coder library of matlab. HDL coder transforms simulink model to fine and synthesizable very high speed integrated circuit hardware description language. Synthesis and execution tools generate bit stream of the code and it is executed to flash memory of FPGA. The developed prototype FPGA digital controller is validated on a 3-level inverter that energize three-phase open-end winding induction motor. Excellent harmonic performance is observed in real time spectral analysis of phase voltage and current of the motor. This development technique enables engineers to construct, simulate, authenticate and develop advanced schemes within small duration of time.
A Domain Specific ESA Inspired Approach for Document Semantic Description

Document semantic similarity is a current research field, in particular when the concept-based characterization (or signature) of the entities should be automatically extracted from their content. This becomes critical whenever someone would like to build an effective recommender system on top of this similarity measure and its usage for document retrieval and ranking. In this work, our research goal is an expert system for job placement, based on skills, capabilities, areas of expertise present into someone’s curriculum vitae and personal preferences. The challenge is to take into account all the personal educational experiences (formal, informal, and on-the-job), but also work-related know-how, to create a concept based profile of the person. This will allow a reasoned matching process with existing job positions, but also towards additional educational experience for profile improvement. Taking inspiration from the explicit semantic analysis (ESA), we developed a domain-specific approach to semantically characterize documents and to compare them for similarity. Thanks to an enriching and a filtering process, we transform the general purpose German Wikipedia dump into a domain specific model for our task. The domain is defined also through a German knowledge base of description for educational experiences and for job offers. Initial testing with a small set of documents demonstrated that our approach covers the main requirements. There are still open issues that we would like to tackle in the next project steps. Alongside, we have other research directions we plan to take into account, ranging from the consideration of information granulation theories to the best parameters set for algorithm tuning, till the extensibility of our solution to a multi-lingual context.
Design of discrete-time current controllers for induction motor drives based on an individual channel analysis approach

In this work a design method for discrete-time current controllers in induction motor drives is addressed. The method of individual channel analysis and design is used in order to represent the IM model as two single-input single-output (SISO) linear systems. Then, based on the obtained transfer functions, the controllers are designed in order to control the original multiple-input multiple-output system as if they were two SISO systems decoupled from each other. Therefore, the method allows to apply classical control techniques for the design of the controllers and simplify the analysis of the system. By following this approach, simple deadbeat controllers are proposed, analyzed and validated by simulation results.
Correcting Electric Motor Drive Speed of Plate Mill Stand in Profiled Sheet Rolling

The process of profiled sheet rolling on a plate mill is studied in the given article. It shows the rolling technology with automated forming of the 'dog bone' profile held on a 5000 mill. There is a description of the system of automated regulation of thickness and control scheme for hydraulic screwdown mechanisms (SM) of a reverse stand. It is shown that the drawback of the rolling control algorithm in passes with profiling is absence of speed correction of electric motor drive horizontal rollers when SM are moved. There is a way of automatic speed regulation of electric motor drives in the function of changing the gap of rollers. There are given oscillograph records of hydraulic SM coordinates and rolling parameters in passes with profiling. Effectiveness of the suggested control method is confirmed.
TDN: An Integrated Representation Learning Model of Knowledge Graphs

Knowledge graph (KG) is playing an important role in many artificial intelligence applications. Representation learning of KGs aims to project both entities and relations into a continuous low-dimensional space. The representation learning technique based on embedding has been used to implement the KG completion, which aims to predict potential triples (head, relation, and tail) in KG. Most current methods concentrate on learning representations based on triple information while ignoring integrating the textual knowledge and network topology of KG. This leads to ambiguous completions. To address this problem and implement more accurate KG completion, we propose a new representation learning model, TDN model, which integratedly embeds the information of triples, text descriptions, and network structure of KG in a low-dimensional vector space. The framework of TDN is defined and the methodology of implementing TDN embedding is explored. To verify the effectiveness of the proposed model, we evaluate TDN via the experiments of link prediction on the real-world datasets. The experimental results confirm the above claims and show that TDN-based embedding significantly outperforms other baselines.
Two-Chambered Chitosan Nerve Guides With Increased Bendability Support Recovery of Skilled Forelimb Reaching Similar to Autologous Nerve Grafts in the Rat 10 mm Median Nerve Injury and Repair Model

Tension-free surgical reconstruction of transected digital nerves in humans is regularly performed using autologous nerve grafts (ANGs) or bioartificial nerve grafts. Nerve grafts with increased bendability are needed to protect regenerating nerves in highly mobile extremity parts. We have recently demonstrated increased bendability and regeneration supporting properties of chitosan nerve guides with a corrugated outer wall (corrCNGs) in the common rat sciatic nerve model (model of low mobility). Here, we further modified the hollow corrCNGs into two-chambered nerve guides by inserting a perforated longitudinal chitosan-film (corrCNG[F]s) and comprehensively monitored functional recovery in the advanced rat median nerve model. In 16 adult female Lewis rats, we bilaterally reconstructed 10 mm median nerve gaps with either ANGs, standard chitosan nerve guides (CNGs), CNGs (CNG[F]s), or corrCNG[F]s (n = 8, per group). Over 16 weeks, functional recovery of each forelimb was separately surveyed using the grasping test (reflex-based motor task), the staircase test (skilled forelimb reaching task), and non-invasive electrophysiological recordings from the thenar muscles. Finally, regenerated tissue harvested from the distal part of the nerve grafts was paraffin-embedded and cross-sections were analyzed regarding the number of Neurofilament 200-immunopositive axons and the area of newly formed blood vessels. Nerve tissue harvested distal to the grafts was epon-embedded and semi-thin cross-sections underwent morphometrical analyses (e.g., number of myelinated axons, axon and fiber diameters, and myelin thicknesses). Functional recovery was fastest and most complete in the ANG group (100% recovery rate regarding all parameters), but corrCNG[F]s accelerated the recovery of all functions evaluated in comparison to the other nerve guides investigated. Furthermore, corrCNG[F]s supported recovery of reflex-based grasping (87.5%) and skilled forelimb reaching (100%) to eventually significantly higher rates than the other nerve guides (grasping test: CNGs: 75%, CNG[F]s: 62.5%; staircase test: CNGs: 66.7%, CNG[F]s: 83.3%). Histological and nerve morphometrical evaluations, in accordance to the functional results, demonstrated best outcome in the ANG group and highest myelin thicknesses in the corrCNG[F] group compared to the CNG and CNG[F] groups. We thus clearly demonstrate that corrCNG[F]s represent promising innovative nerve grafts for nerve repair in mobile body parts such as digits.
An Uni-directional Single-Phase Soft-switched Resonant High Frequency Link Inverter

Renewable and alternate energy grid integration is an area of interest in the recent years due to the advancements made in Solar and Fuel Cells. This paper considers a single-stage single phase uni-directional High-Frequency Transformer (HFT) link DC-AC converter for grid integration of Distributed Energy Resources (DER). The HFT helps by providing galvanic isolation to protect DER from single faults at significantly low cost and size in comparison with Low Frequency Transformer (LFT). Resonant based power conversion in relatively high voltage DCDC converters results in higher efficiency and power density. Soft switching due to device capacitances and HFT parasitic leakage inductances enables high frequency of operation. This paper presents a novel Parallel Resonant Converter (PRC) based control strategy that results in high quality adjustable frequency and amplitude AC and soft switching of all devices in the entire range of operation. A well-known single stage isolated inverter topology is slightly modified to incorporate PRC based operation. Simulation results are presented to validate the topology and proposed modulation scheme.
Strategies for Creating, Generalising and Transferring Design Science Knowledge - A Methodological Discussion and Case Analysis

Design Science Research has been well accepted as part of Information Systems Research. The discussion about the research process and the structure of design theories has been going on for some time. While research has been done on the relation between design theories and other types of theories, not much has been said about how design knowledge can be re-used. Other disciplines refer to such re-use as “generalisation” and “transfer”. We define a three-level separation of design abstraction (short-, mid-, and long-range) and show how knowledge re-use strategies operate between and within them, as well as how they relate to generalisation and transfer. Each strategy is supported by a case from an existing publication, showing that the types of design theories and the research strategies can be found in practice. We argue that these research strategies can provide guidelines to researchers and reviewers for planning, performing and evaluation Design Science Research.
CLARA: Cloud Learning Autonomous Reacting Algorithms, a Machine Learning Service for the Internet of Things

The Internet of Things (IoT) is growing and it is expected that, by 2020, 31 thousand million devices will be connected. By that time, more than 7 ZetaBytes will have been sent to Cloud Services to be stored and analysed. In order to find errors, analyse and learn from all the data acquired, Machine Learning (ML) Models are being used. Cloud Service providers offer tools to simplify the development of ML Models. However, the developers still have to be involved in the development, testing, and pruning of the models. CLARA is a cloud based Machine Learning service that aims to simplify all the current developers’ work by creating a data interpreter that is able to generate the best ML model for the data provided. In the following paper, research is presented that shows how CLARA can be used to receive and learn from data coming from IoT applications. Furthermore, it will show how CLARA obtains better results than other ML models trained by a developer with no theoretical background on ML.
Building a Machine Learning Model for the SOC, by the Input from the SOC, and Analyzing it for the SOC

This work demonstrates an ongoing effort to employ and explain machine learning model predictions for classifying alerts in Security Operations Centers (SOC). Our ultimate goal is to reduce analyst workload by automating the process of decision making for investigating alerts using the machine learning model in cases where we can completely trust the model. This way, SOC analysts will be able to focus their time and effort to investigate more complex cases of security alerts. To achieve this goal, we developed a system that shows the prediction for an alert and the prediction explanation to security analysts during their daily workflow of investigating individual security alerts. Another part of our system presents the aggregated model analytics to the managers and stakeholders to help them understand the model and decide, on when to trust the model and let the model make the final decision. Using our prediction explanation visualization, security analysts will be able to classify oncoming alerts more efficiently and gain insight into how a machine learning model generates predictions. Our model performance analysis dashboard helps decision makers analyze the model in signature level granularity and gain more insights about the model.
Semantic Segmentation of 3D Point Cloud to Virtually Manipulate Real Living Space

This paper presents a method for the virtual manipulation of real living space using semantic segmentation of a 3D point cloud captured in the real world. We applied PointNet to segment each piece of furniture from the point cloud of a real indoor environment captured by moving a RGB-D camera. For semantic segmentation, we focused on local geometric information not used in PointNet, and we proposed a method to refine the class probability of labels attached to each point in PointNet’s output. The effectiveness of our method was experimentally confirmed. We then created 3D models of real-world furniture using a point cloud with corrected labels, and we virtually manipulated real living space using Dollhouse VR, a layout system.
Using neural networks to estimate the degree of wetlands with remote sensing data

In paper the problem of estimation the degree of wetlands on basis of remote sensing data is considered. For this purpose digital image processing is used. The satellite images are used as the main data. In paper set of local and global features is selected for segmentation area on two classes: water surface and land surface. For each point of color satellite image segment is determined whether the corresponding portion of the area to the water surface. Since the task set is described in general terms and not be formalized, segmentation method is based on machine learning. For processing of remote sensing data neural network is used. The using of neural networks for processing of remote sensing data can automate the task of analysis and assessment of areas of water bodies. The proposed solutions have been successfully used to assess the Oka river basin in Nizhny Novgorod and Vladimir regions.
A Five-Parameter Analytical Curvefit Model for Open-Circuit Voltage Variation with State-of-Charge of a Rechargeable Battery

Rechargeable batteries, particularly using lithiumion chemistry, are used for energy storage in a wide range of applications. As these batteries charge and discharge, the variation of open-circuit voltage (OCV) is highly non-linear with respect to state-of-charge (SOC). Modelling this phenomenon using the details of the internal electrochemical processes results in a highly complex model that can be hard to build and simulate. In order to focus on the terminal behaviour only, this paper presents a simple five-parameter analytical model that accurately curvefits empirical data of OCV variation with battery SOC. The proposed model uses a summation of a logarithmic function and a polynomial function to capture the non-linearities in the low-SOC and high-SOC regions, respectively. The extent of nonlinearity or bend in each of these two regions can be controlled using separate parameters. Comparison with experimental data during discharge of a Samsung 18650 cell is shown. The proposed battery model is simple and flexible, and can easily cater to a variety of battery types.
Cooperative Energy Scheduling of Microgrids with Renewable Energy Integration

This paper proposes an energy management framework for a distribution system comprising of multiple cooperative microgrids. The energy management framework aims at reducing the energy cost of the microgrids by minimizing the grid purchases which comprises of conventional generation. The intermittencies associated with renewable energy based-energy sources are modelled in the proposed framework through scenarios. Within this stochastic framework, a power exchange model is embedded that allows local power exchanges between neighboring microgrids, with the objective of optimally alleviating the distribution power losses. Extensive numerical results are shown to corroborate the effectiveness of the proposed framework.
Remote Monitoring of Human Vital Signs Using mm-Wave FMCW Radar

Electromagnetic radars have been shown potentially to be used for remote sensing of biosignals in a more comfortable and easier way than wearable and contact devices. While there is an increasing interest in using radars for health monitoring, their performance has not been tested and reported either in practical scenarios or with acceptable low errors. Therefore, we use a frequency modulated continuous wave (FMCW) radar operating at 77 GHz in a bedroom environment to extract the respiration and heart rates of a patient, who is used to lying down on the bed. Indeed, the proposed signal processing contains advanced phase unwrapping manipulation, which is unique. In addition, the results are compared with a reliable reference sensor. Our results show that the correlations between the reference sensor and the radar estimates are in 94% and 80% for breathing and heart rates, respectively.