Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Damping Method for Harmonic Current Mitigation in Grid-Connected MMC-HVdc Applications

This paper presents an active damping method to mitigate specific harmonics by means of an enhanced control strategy for Modular Multilevel Converters (MMC). Compared to low level Voltage Source Converters (VSC), the complexity of MMCs reveals additional degrees of freedom and therefore enables implementation of advanced control features. As penetration of power electronic devices within the European ac grid rises, MMC control strategies including network services seem beneficial. Hence, active harmonic damping is implemented in this paper. This measure additionally reduces stresses of high voltage resources.While common active damping methods for HVdc applications based on signal filters use combinations of lead-lag and notch filters to eliminate various resonant frequencies, this paper focusses on the extraction of grid specific harmonics by means of multiple complex coefficient filters. Thus, mitigation of specific harmonics is implemented in a closed control loop, whereby negative interactions with other damping facilities in the grid are avoided and system stability is improved.
[Invited] Quality Assurance of Machine Learning Software

Functionalities of machine learning software are dependent on a set of data input to them; a slight change in a training dataset has much impact on learning parameter values and thus on inference results. ML-based systems bring about a new challenge to quality assurance methods. This paper reviews two traditional views of service and product qualities. Furthermore, it introduces a platform view, in which co-creation of value is a major concern.
Fast Approximate Algorithm for the Single Source Shortest Path with Lazy Update

In this paper, we propose a fast approximate algorithm of the Dijkstra algorithm which solves the single source shortest path problem(SSSP). In the conventional Dijkstra algorithm, one node with the minimum tentative distance is selected from unvisited nodes, and the tentative distances of its adjacent nodes are relaxed. In this method, its optimality is guaranteed by utilizing the fact that the tentative distance of the selected node will not be updated any more. However, there may be several nodes whose tentative distance will be updated. In this paper, we reduce the computation time by using the Lazy update method which selects all of these nodes and determines their distances simultaneously. Moreover, if the error is allowed, it becomes much faster. By experiments, we confirm that it achieves about 10 times faster.
Design of Medium Voltage DC Grids– Impact of Power Flow Control on Grid Structure

In medium voltage direct current (MVDC) grids converters allow power flow control. However, in existing medium voltage alternating current grids such a power flow control is not possible due to an unsuitable grid structure which is mostly radially operated. Therefore, this paper answers the question on how the power flow controllability of converters affects a suitable grid structure of MVDC grids. First, this paper analyzes the possibility and potential benefit of power flow control. After this, the resulting mathematical optimization problem for the power flow control is presented. It represents a quadratically constrained quadratic problem which is cohesively formulated and explicitly includes load flow equations. To consider power flow in the grid structure designing process, this operation optimization problem is integrated in a method for designing MVDC grids. Based on an exemplary supply task, grid structure is designed in four investigations by varying different power flow control relevant parameters. Results show that there is a trade-off between the benefit as well as costs of power flow control and an extensive grid infrastructure. Furthermore, a higher meshed grid structure reduces the need for power flow control and for high converter dimensioning. Separated grids with only one primary substation avoid additional loadings of high voltage level.
Detection of Human Motion Gestures Using Machine Learning for Actual Emergency Situations

Wearable devices are widely used in daily lives. Hence, it is expected that wearable devices will be used in nursing care for elderly patients. However, a very rapid response is necessary under various emergency situations. It is necessary to contact a family guardian to quickly care for elderly family members. This study focuses on the detection of human motion gestures based on an acceleration sensor. Specifically, the study aim involves recognizing arbitrary hand gestures. Sufficient accuracy was obtained in the case of the Hiragana dataset. It is expected that the proposed approach offers a scalable technique for actual emergency situations.
Magnetic-Field-Dependent Electrical Tree Under Impulse-Superimposed DC Voltage at Low Temperature

Cross-linked polyethylene (XLPE) is an important insulating material in warm dielectric superconducting cable. When it is operated in a high electric field, an electrical tree may occur and cause insulation breakdown. In this paper, the XLPE samples were prepared and a needle-plate electrode was employed. The treeing process at 0, -30, and -60 °C was recorded. The magnetic field applied on the samples was 0, 200, 400, and 600 mT. An electrical tree was initiated by an impulse-superimposed dc voltage. Two types of tree structures were found and their growth processes were recorded. In addition, the fractal dimension and accumulated damage were both calculated and analyzed. The result indicates that there is a magnetic field threshold to accelerate the tree growth, which increases with the decrease in temperature. Above -60 °C, it is below 600 mT and turns out to be higher than 600 mT at -60 °C. The magnetic field influences the tree-growth process by affecting the space-charge distribution.
Cooperative Adaptive Cruise Control Using Turn Signal for Smooth and Safe Cut-In

This paper proposes a method to reduce the risk of collision in cut-in situations, which enhances the performance of Cooperative Adaptive Cruise Control (CACC) in terms of safety. In the proposed control method, the turn signal of the cut-in vehicle is used to create a virtual vehicle such that the cut-in is handled smoothly and safely in advance. This method increases the safety of the CACC system in cut-in situations with a platoon of vehicles.
Temperature and Pressure Dependence of Gas Permeation in a Microporous Tröger’s Base Polymer

Gas transport properties of PIM-EA(H2)-TB, a microporous Tröger’s base polymer, were systematically studied over a range of pressure and temperature. Permeability coefficients of pure CO2, N2, CH4 and H2 were determined for upstream pressures up to 20 bar and temperatures up to 200 °C. PIM-EA(H2)-TB exhibited high permeability coefficients in absence of plasticization phenomena. The permeability coefficient of N2, CH4 and H2 increased with increasing temperature while CO2 permeability decreased with increasing temperature as expected for a glassy polymer. The diffusion and solubility coefficients were also analysed individually and compared with other polymers of intrinsic microporosity. From these results, the activation energies of permeation, diffusion and sorption enthalpies were calculated using an Arrhenius equation.
Distributed Transmit Beamforming Expanding the Capacity and Range of Tactical Communications

Phased array antenna technologies have been used for communications and Intelligence, Surveillance and Reconnaissance (ISR) for many decades to create steerable beams that more efficiently focus energy in desired directions. Generally they have been based on the classical approach of fixed antenna elements evenly spaced at some fraction of a wavelength, all connected to a single transceiver. Distributed transmit beamforming is a more recent approach that does not conform to these design constraints. Individual radios are array elements that are dispersed far beyond a single wavelength from each other and which are allowed to move around the battlespace, with the distributed transmit beamforming protocol maintaining the necessary synchronisation between array nodes to ensure energy is concentrated on a distant receiver. In this paper we report on progress to develop an implementation of distributed transmit beamforming that is based on the receiver providing explicit feedback of channel information to the array nodes using a separate backchannel, allowing for high speed, accurate frequency and phase synchronisation of nodes to build an array as required.
Resting-State Default Mode Network Related Functional Connectivity Is Associated With Sustained Attention Deficits in Schizophrenia and Obsessive-Compulsive Disorder

Background: Previous studies have indicated the resting-state default mode network (DMN) related connectivity serving as predictor of sustained attention performance in healthy people. Interestingly, sustained attention deficits as well as DMN-involved functional connectivity (FC) alterations are common in both patients with schizophrenia (SCZ) and with obsessive-compulsive disorder (OCD). Thus, the present study was designed to investigate whether the DMN related resting-state connectivity alterations in these two psychiatric disorders were neural correlates of their sustained attention impairments. Methods: The study included 17 SCZ patients, 35 OCD patients and 36 healthy controls (HCs). Sustained attention to response task was adopted to assess the sustained attention. Resting-state scan was administrated and seed-based whole-brain FC analyses were performed with seeds located in classical DMN regions including bilateral medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). Results: Both SCZ and OCD patients had poorer sustained attention than HCs. Sustained attention deficits in OCD was negatively correlated with their impaired FC of right mPFC-left superior frontal gyrus (SFG) within DMN, and that in SCZ was significantly correlated with their altered FC of left mPFC-bilateral anterior cingulate cortex (ACC) which indicated interaction between DMN and salience network. In addition, the FC between left mPFC and right parietal lobe indicating the interaction between DMN and frontal-parietal network was correlated with sustained attention in both SCZ and OCD. Conclusion: These findings suggest the importance of DMN-involved connectivity, both within and between networks in underlying sustained attention deficits in OCD and SCZ. Results further support the potential of resting-state FC in complementing information for cognitive deficits in psychiatric disorders.
Operant Costs Modulate Dopamine Release to Self-Administered Cocaine

The costs associated with obtaining illicit drugs can fluctuate depending upon the relative drug availability. As a consequence of the changing costs, the effort that one must exert to obtain drugs is dynamic. Considerable evidence illustrates a critical role for dopamine in the ventral medial striatum in mediating drug reinforcement. However, little is known regarding how dopamine release is affected by changes in the costs associated with earning drugs. We used fast-scan cyclic voltammetry to determine how changes in the operant requirement affected dopamine release to self-administered cocaine in male rats. Dopamine release to cocaine infusions increased across trials during self-administration sessions using a fixed-ratio reinforcement schedule with a low operant requirement. However, increasing the operant requirement abolished the within-session elevation in dopamine release to drug rewards. This effect was not due to underlying changes in preinfusion dopamine levels and was not explained by cocaine levels in the brain. This within-session increase in dopamine release to cocaine infusions reemerged when the operant requirement was lowered. Under a progressive ratio reinforcement schedule, there was no increase in dopamine release to drug rewards across trials, which contrasts with prior studies demonstrating an increase in dopamine release to food rewards. Collectively, these findings illustrate that the influence of operant costs on reward-evoked dopamine release depends upon type of reward that can be earned (e.g., food or drug). SIGNIFICANCE STATEMENT The mesolimbic dopamine system is involved with mediating drug reinforcement. Although the costs associated with earning drugs are dynamic, no studies to date have examined how dopamine release to drug rewards is affected by changing costs. By performing fast-scan cyclic voltammetry recordings in rats self-administering cocaine, the present work demonstrates that changing the operant costs reversibly modulates the dopamine response to cocaine rewards. Furthermore, these findings highlight that the influence of costs on dopamine release to drug rewards differs from the established effect of costs on dopamine release to food rewards.
Quantification of Wind Farm Operational and Infrastructure Resilience

Increase in catastrophic weather events, has pushed the research community to study power system resilience in recent times. Resilience planning of power system has become inevitable now a days to minimize the damage caused by low probability but very high impactful weather events. This paper presents a new approach to quantify resilience of a wind farm (WF). In this study “Resilience Pentagon” has been introduced to study different time dependent stages of a WF during and after the severe weather events. In this context both operational and infrastructure resilience of a WF have been discussed. A new weather and time dependent Monte Carlo simulation has been developed to estimate the probable number of damaged WTs during sever windstorm and calculate both types of resilience of WF. The proposed framework has been applied to a large scale test case WF to measure its resilience. Simulation results probabilistically quantify the both types of the resilience considering with and without active yawing control action of WTs during severe wind storm.
Terrestrial isopods (Oniscidea) of the White Carpathians (Czech Republic and Slovakia)

Abstract This paper summarizes data regarding the terrestrial isopods of the White Carpathians range in the Western Outer Carpathians based on field research undertaken during the past several decades in natural meadow pasture and forest localities. Using a combination of four collection methods 19 species belonging to nine families were recorded. The most common representatives were ProtracheoniscuspolitusTrachelipusrathkii and Ligidiumhypnorum. In general the biodiversity of isopod communities in the studied area was considerable with half of the localities explored inhabited by six to ten species. The composition of the isopod assemblages was determined by the character of the biotope and its geographical location. Forest habitats were considerably richer in species than the meadow and pasture ecosystems. Some xerotermic localities in the Slovak part of the area were inhabited by Trachelipusnodulosus and Orthometoponplanum thermophilic species typically associated with warmer parts of Europe. Two relic species (Hyloniscusmariae and Ligidiumgermanicum) were confirmed for this area. Except for only one finding of Porcellioscaber no other evidently introduced or synanthropic species were recorded. Based on the data analyzed the high nature conservancy value of the given area is emphasised.
Effective Attention Networks for Aspect-level Sentiment Classification

This paper deals with the aspect-level sentiment classification which identifies the sentiment polarity of a specific aspect of its context. We introduce novel attention networks by using the benefits of Long Short-Term Memory (LSTM), Attention mechanisms and Lexicons to form an aspect-specific representation. Though a variety of neural network models have been proposed recently, however, previous models have captured the importance of aspects in their contexts and developed various methods by modeling their contexts via generating aspect representations. In this paper, aspects and their contexts are treated separately and learned their own representations. Additionally, the purpose of lexicons is to highlight the important sentiment words of aspects and their contexts. The relation between aspects and their contexts are explored by concentrating on different parts of a sentence when different aspects are taken as input. We evaluate our models on Laptop and Restaurant datasets and show that our approaches improve classification accuracy in aspect-level sentiment classification.
Power Signal Classification with Combinational Spectrogram-based CNN for Embedded System Health Management

This paper addresses the problem of the embedded system health management for high-speed flight systems. Especially, we focus the variation of power signals used in embedded systems because the electrical degeneration is strongly related to the power levels and frequencies. If the power signals can be classified into normal status and abnormal status, the sudden electrical degeneration of embedded systems can be successfully detected. The conventional threshold-based classification which has been used in aerospace and defense fields cannot find out the hidden anomaly within the thresholds. This paper proposes an accurate power signal classification method using combinational spectrogram-based convolutional neural networks (CNN). The power signals are combined with eigenvalues and converted to spectrogram which can analyze them on time and frequency domain simultaneously. Then, the CNN for power signal classification is trained and validated using the combinational spectrograms. Inference results showed that the proposed method can accurately classify the power signals into normal status and abnormal status.
MDACache: Caching for Multi-Dimensional-Access Memories

For several emerging memory technologies, a natural formulation of memory arrays (cross-point) provides nearly symmetric access costs along multiple (e.g., both row and column) dimensions in contrast to the row-oriented nature of most DRAM and SRAM implementations, producing a Multi-Dimensional-Access (MDA) memory. While MDA memories can directly support applications with both row and column preferences, most modern processors do not directly access either the rows or columns of memories: memory accesses proceed through a cache hierarchy that abstracts many of the physical features that supply the aforementioned symmetry. To reap the full benefits of MDA memories, a co-design approach must occur across software memory layout, the mapping between the physical and logical organization of the memory arrays, and the cache hierarchy itself in order to efficiently express, convey, and exploit multidimensional access patterns. In this paper, we describe a taxonomy for different ways of connecting row and column preferences at the application level to an MDA memory through an MDA cache hierarchy and explore specific implementations for the most plausible design points. We extend vectorization support at the compiler level to provide the necessary information to extract preferences and provide compatible memory layouts, and evaluate the tradeoffs among multiple cache designs for the MDA memory systems. Our results indicate that both logically 2-D caching using physically 1-D SRAM structures and on-chip physically 2-D caches can both provide significant improvements in performance over a traditional cache system interfacing with an MDA memory, reducing execution time by 72% and 65%, respectively. We then explore the sensitivity of these benefits as a function of the working-set to cache capacity ratio as well as to MDA technology assumptions
Development of a neuron-inspired tactile information processing model

Tactile information processing is essential to robotic manipulation such as grasping, lifting and sliding. We developed a tactile signal processing model by mimicking the firing activity of sensory afferents including slowly adapting (SA) and fast adapting (FA) afferents. This model was validated in publically available data by the classification of the type of nine objects from tactile sensor pressure signals while a robot grasped and releases each object. The classification performance of the developed model was compared with traditional methods based on raw sensor signals, spectral analysis, or statistical analysis. Our model performed better than other models when only one of the tactile sensor data were used and similar to the best model when all the tactile sensor data were used. The proposed model could provide an alternative means to process tactile information in a robotic hand.
A Brazilian Speech Database

This work introduces a Brazilian Speech Database (BrSD), a novel dataset freely available created to support the development of speech-based recognition tasks. As far as we know, this is the first Portuguese language based database with these characteristics created and made available to the research community. We also describe experiments accomplished on BrSD exploring its different possibilities of classification tasks, i.e., age group and gender classification. We use four well-known acoustic features extracted directly from the audio signal and one texture-based feature extracted from a visual representation of the audio signal, the spectrogram. We considered three different classification scenarios: each feature individually, early fusion of the features, and late fusion of the features. Experiments were conducted using Support Vector Machine (SVM) and Multi-layer Perceptron (MLP) classifiers. The obtained results showed that SVM classifier achieved the best recognition rates both in early and late fusion scenarios. The best recognition rates achieved were 91.25%, 88.75%, and 80.25% for gender, age group, and age-gender classification tasks, respectively.
Large Size Painting with Infraless Vision-aided Mobile Robot

This paper presents two design principles for developing LSP-Bot (large size painting mobile robot); visual error compensation and wide marker system. Localization error is compensated by vision to enhance the precision of wheel odometry and IMU sensors. The robot corrects the accumulated local error in position using downward-facing camera and sets the global alignment using another backward-facing camera. In order to compensate visual error robustly in lots of tests, a wide marker system is developed. Implementing these technologies, the robot can draw larger and more well-drawn painting without any infra or frame structures.
Development of an Autonomous Mobile Assistant: RoboMuse 4.0

Over the years, mobile robots have proven to be of significant assistance to humans in various domains including domestic applications, agriculture, manufacturing, and defense. In this paper, we have focused on a domestic application, more specifically the development of a custom designed low cost mobile robot - RoboMuse 4.0, into a highly capable mobile assistant. From following a person around carrying his or her load, to acting as a mobile guide which is capable of autonomously charging itself by docking into a charging station, we have concentrated on automating the mobile functions of an assistant. This was achieved through various subtasks including simultaneous localization and mapping, autonomous exploration, point to point navigation, and autonomous docking and charging. The performance of the robot was also evaluated through various established and customized tests, the results of which have also been included in this paper.