Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Split-Phase Nine-Switch Inverter with Reduced Power Losses for 800 Volts Traction Application

Wide band-gap (WBG) devices operating at higher switching frequencies have become attractive for EV traction inverter application in the recent years as they can be used to reduce harmonic distortion and switching loss. On the other hand, 800 V EV battery drivetrain systems have been recently deployed in commercially available in premium electric vehicles in order to achieve higher power for propulsion and faster charging. Given this scenario, this paper puts an effort to investigate and compare a 2-level inverter, a 3-level hybrid inverter and a nine-switch inverter for such an application. Motivation for investigating each of the inverter types is provided in the respective sections. This paper utilizes a novel hybrid modelling approach to get deeper insights into the power loss of inverter with device non-linearity and the harmonics, torque ripple and magnetic saturation in the motor due to different inverter types and their outputs. Cosimulation has been performed on each of the inverter types driving the same 100 kW surface-mounted permanent magnet motor on a WLTC drive cycle and results are discussed.
Precise hand-guiding of redundant manipulators with null space control for in-contact obstacle navigation

Rand-guiding of collaborative redundant manipulators allows an unskilled user to interact and program the robot intuitively. Many industrial applications require precise positioning at the end-effector (EEF) level inside cluttered environments, where manipulator's redundancy is required. Yet, the potentialities of redundancy while hand-guiding at EEF level are not fully explored. This paper addresses the subject of precision in hand-guiding at EEF level while using the redundancy for in-contact obstacle navigation. In the presence of a contact with an obstacle, the proposed null space control method actuates in a way that the manipulator slides compliantly with its structure on the body of the obstacle while preserving the precision of the hand-guiding motion at EEF level. Force/torque (FT) data from a FT sensor mounted at the robot flange are the input for EEF precision hand-guiding while the torque data from the joints of the manipulator represent the contact between robot structure and obstacles. Experimental tests were carried out successfully using a KUKA iiwa industrial manipulator with 7 degrees of freedom (DOF). Where, the EEF is hand-guided on a straight line while the robot is sliding on the obstacle with its structure, results indicate the precision of the proposed method.
A Versatile Workbench Simulator: Five-phase Inverter and PMa-SynRM performance evaluation

This paper presents the design and structure of a versatile workbench simulator for evaluating the performance of a five-phase inverter and Permanent Magnet assisted Synchronous Reluctance Motor (PMa-SynRM). The simulator allows for adding variations to the modulation techniques, changing the inverter structure's semiconductor device, and calculating the inverter's power losses. It can also facilitate observing the current, voltage, and the joint temperature of the semiconductors devices. Furthermore, we can obtain a perform that is close to an actual PMa-SynRM, depending on the desired conditions of speed and torque. The workbench simulator was developed by combining three software: Matlab/Simulink, PLECS and Altair Flux.
Optimization of Active Power Dispatching Considering Lifetime Fatigue Load for Offshore Wind Farm Based on Multi-agent System

As a rapidly developing renewable energy source, effective active power dispatching for wind power has become common. For offshore wind farms with high maintenance costs, balanced fatigue loads among wind turbines need to be considered in order to reduce the number of maintenances. Based on this target, this paper proposes an optimized power distribution function to balance the fatigue difference among wind turbines. At the same time, this paper adopts the structure of the unsupervised multi-agent system (MAS) in the offshore wind farm, the communication between wind turbines is used to complete the allocation of power commands. The simulation results show that the wind farm with MAS structure can effectively deliver the power commands. And the optimized power distribution function can reduce the fatigue difference of the wind turbines after longterm operation.
Miniaturized piezo-actuated adaptive prism for biaxial optical scanning

We present a biaxial adaptive prism that allows for a simplified light path compared to scanning mirrors. Using three piezo bimorph actuators with stepless control, we actively tilt a fluid-glass interface, achieving scan angles up to 8.5° with response times down to 5.5 ms. The compact and optimized design has an outer diameter of 33 mm for a 7 mm aperture.
Context in Human Emotion Perception for Automatic Affect Detection: A Survey of Audiovisual Databases

An important aspect of human emotion perception is the use of contextual information to understand others' feelings even in situations where their behavior is not very expressive or has an emotionally ambiguous meaning. For technology to successfully detect affect, it must mimic this human ability when analyzing audiovisual input. Databases upon which machine learning algorithms are trained should capture the context of social interactions as well as the behavior expressed in them. However, there is a lack of consensus about what constitutes relevant context in such databases. In this article, we make two contributions towards overcoming this challenge: (a) we identify two principal sources of context for emotion perceptions based on psychological theory, and (b) we provide an overview of how each of these has been considered in published databases covering social interactions. Our results show that a similar set of contextual features are present across the reviewed databases. Between all the different databases researchers seem to have taken into account a set of contextual features reflecting the sources of context seen in psychological theory. However, within individual databases, these features are not yet systematically varied. This is problematic because it prevents them from being used directly as resources for the modeling of context-sensitive affect detection. Based on our findings, we suggest improvements for the future development of affective databases.
Recent Advances at the International Fusion Materials Irradiation Facility IFMIF

As the qualification of materials under fusion-specific conditions is inevitable for the design, performance, and safe operation of fusion power reactors, timely availability of a suitable intense neutron source has become a major element in fusion strategy scenarios. In response to this need ,a basic design for the acceleratordriven International Fusion Materials Irradiation Facility (IFMIF) has been developed under the umbrella of the IEA’s Executive Committee on Fusion Materials within the “Conceptual Design Activity” phase (1995-96). This design was modified substantially during the “Conceptual Design Evaluation” phase (1997-98) and in 1999, mainly to meet global requirements of cost reduction. The main objectives of the current “Key Element Technology” phase (2000-2002) are to reduce any technology risk factors and to verify relevant component designs on a laboratory scale. A major result from recent neutron transport calculations and targeted design optimizations is that the high-flux test volume could be increased by ~20% and that for structural materials fusion reactorrelevant irradiation parameters like the H/dpa rate, He/ dpa rate or recoil energy distribution are perfectly represented by IFMIF over the entire test volumes. Significant progress in accelerator systems includes 1000 h continuous operation of an intense D source, detailed beam dynamics design by separate groups verifying IFMIF performance, and reliability tests of a diacrode RF power amplifier at 1 MW for 1000 h. With respect to the lithium-target area, substantial advances have been made during the KEP phase, including a detailed integral design based on well-founded thermohydraulic analyses and various experiments on existing loops dedicated to impurities and potential corrosion issues. Preparations are presently made to enter an “Engineering Validation and Engineering Design Activity” (EVEDA) phase that includes the experimental verification of major subsystems operation as well as the establishing of an engineering design for licensing and construction.
Efficient Data Collection in Large-Scale UAV-aided Wireless Sensor Networks

Dispatching unmanned aerial vehicles (UAVs) to collect sensor data from distributed sensor networks is expected to significantly improve the data collection efficiency of traditional Wireless Sensor Networks (WSNs). In this paper, we consider data collection for distributed large-scale UAV-aided WSNs. We first divide the target area into clusters, and determine the cluster head election and data forwarding rules within the cluster based on the value of information (VoI) and power of nodes. Then sensor nodes collect information around by event-driven, and the UAV acts as a mobile sink to collect the data of cluster head nodes. The direct future prediction (DFP) model is used to plan the UAV path, which allows the UAV to handle multitarget tasks, maximizing the total VoI collected while ensuring that the UAV is charged at low power. Simulation results show that our DFP model is superior to traditional Q-Learning and Deep Q-Learning Network (DQN) in performance.
Coupled Inductor Based High Step-up Hybrid Inverter for Microgrid Application

Presence of right half plane zero (RHPZ) in conventional boost and buck-boost derived hybrid converters makes the system non minimum phase and it becomes difficult to obtain adequate phase margin along with higher bandwidth for such systems. Moreover, for microgrid applications, these need to operate on very high duty ratio in order to obtain high voltage gain, which causes severe reverse recovery of the diodes and increased EMIs. This paper introduces a high step-up hybrid inverter having no RHPZ for hybrid microgrid. The proposed hybrid inverter is developed by coupling the filter inductors of a quadratic boost converter with an insertion of damping network in the circuit and replacing the load side main switch by the H-bridge inverter. This arrangement helps in complete elimination of RHPZ and thereby makes the system minimum phase. The proposed hybrid inverter gives high step-up de voltage with comparatively low duty ratio and also provides simultaneous AC and DC outputs with inherent shoot through protection. Detailed mathematical modeling and small signal dynamic analysis are carried out. Experimental and simulation results are presented to validate the proposed hybrid inverter for the hybrid microgrid.
State estimation of energy measurements for enhanced trust in distributed ledger applications on rural microgrids

Distributed ledger technology has the potential to revolutionize the exchange of value in energy supply systems. Trust is central to the records in a ledger and the measurement of energy production or consumption must be dependable for confidence in an energy transaction. The case of measurement accuracy and error detection in a rural micro-grid is investigated for methods to enhance trust in energy exchanges. The suitability of state estimation as a technique is evaluated for mitigating inaccuracies in meter accuracy and therefore increasing trust. Application of the same principle for error detection, either from intentional meter tampering or faulty equipment is outlined. State estimation is found to provide a small improvement to the measurement of energy in a simple radial micro-grid, suggesting that with further work this method could benefit micro-grid operators and users seeking improvements to the accuracy and error detection.
A Four-leg Buck Inverter for Three-phase Four-wire Systems with the Function of Reducing DC-bus Ripples

Three-phase four-wire inverters are usually used to feed unbalanced three-phase loads with neutral currents. The unbalanced three-phase loads also bring to second-order ripples in the DC bus, which should be mitigated by bulky DC-bus capacitors to improve the system performance. In this case, the DC capacitance is designed for the second-order ripple frequency instead of the switching frequency, so it can not be reduced even when SiC MOSFETs are adopted to achieve high switching frequency. Although various topologies of three-phase four-wire inverters has been proposed to provide the path for neutral currents, they cannot handle the second-order ripples. Also, some active power decoupling solutions can be adopted, but they require additional active swithes and components, which increases the cost of the system. In this paper, a four-leg buck inverter is proposed, which consists of four DC-DC buck converters. Each buck converter is independently controlled. This topology can not only provide neutral currents, but also reduce the second-order ripples in the DC bus with active power decoupling control. The proposed topology doesn't require any additional active switches comparing to the conventional topologies with neutral legs. The effectiveness of proposed topology is verified by the simulation in MATLAB/Simulink.
Approximating Global Illumination with Real-Time Ambient Occlusion

Approximating global illumination has long been a problem that real-time applications, such as games, have been trying to solve efficiently. For years, the best and most efficient techniques have been ambient occlusion algorithms based in screen-space, such as Horizon-Based Ambient Occlusion. Although efficient, such algorithms can suffer from artifacts, and a lack of realism due to being screen-space algorithms trying to approximate a world space effect. In 2018, Nvidia released the first ever hardware accelerated ray tracing graphical processing units. Proper ray traced ambient occlusion could now be a viable alternative to screen-space algorithms. However, to be a viable alternative, the results need to be of a certain quality, and its performance must be in the order of a few milliseconds. This thesis will show that although real-time ambient occlusion is possible, and its results are of reasonable quality, it is still not quite ready to be used in real-time applications such as games. This is due to some visual patterns in the results that are caused by the low sample count when calculating ambient occlusion, its sceneand viewpoint-dependent performance, and lastly the time required to rebuild acceleration structures.
Bridging the Usability–Expressivity Gap in Biomedical Data Discovery

During the scientific discovery process, biomedical researchers pose complex questions that require precise answers or relevant datasets for further analysis. Semantic Web technologies provide a solution to structured, expressive, integrated search, but they pose a steep learning curve for domain users. Hence, there is a usability–expressivity gap in biomedical data discovery. In this work, we will look at the translation of Natural Language Queries and Statements to structured triple patterns (head–relation–tail), observed in RDF graphs and SPARQL queries. We generate word embeddings from a large corpus of biomedical literature, taking into consideration different named representations of biomedical entities. We also generate triple pattern embeddings by translating the relations in hyperplanes. We use a neural network architecture to map syntactic elements in a constituencybased parse tree to structured triples. We evaluate the generated word embeddings intrinsically by a visualization of biomedical entities, and extrinsically by classifying each abstract into biomedical topics. We also evaluate the performance of the architecture for NL–triple translation for a drug knowledge base.
Energy-Efficient Optimal Sensing and Resource Allocation of Soft Cooperative Spectrum Sensing in CRNs

Cooperative spectrum sensing (CSS) is an effective method to combat channel fading and improve sensing accuracy. However, cooperation also increases energy consumption, which in turn decreases the network performance. In this paper, we investigate the energy-efficient optimal sensing and resource allocation of soft combing CSS in cognitive radio networks. We aim at maximizing the average energy efficiency (EE) of secondary users while guaranteeing the detection performance and the quality of service of primary user by jointly optimizing the energy detector threshold, number of participating users, sensing time and transmit power. To address the non-convexity of the formulated problem, the expression of optimal energy detector threshold is derived first. Then, a sub-optimal alternating iterative algorithm is proposed to solve the problem efficiently. Simulations demonstrate the superiority and optimality of the proposed algorithm. The results show that our proposed algorithm significantly improve the EE compared with existing schemes.
Gaussian Conditionally Markov Sequences: Dynamic Models and Representations of Reciprocal and Other Classes

Conditionally Markov (CM) sequences are powerful mathematical tools for modeling problems. One class of CM sequences is the reciprocal sequence. In application, we need not only CM dynamic models, but also know how to design model parameters. Models of two important classes of nonsingular Gaussian (NG) CM sequences, called <inline-formula><tex-math notation="LaTeX">$CM_L$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$CM_F$</tex-math></inline-formula> models, and a model of the NG reciprocal sequence, called reciprocal <inline-formula><tex-math notation="LaTeX">$CM_L$</tex-math></inline-formula> model, were presented in our previous works and their applications were discussed. In this paper, these models are studied in more detail, in particular their parameter design. It is shown that every reciprocal <inline-formula><tex-math notation="LaTeX">$CM_L$</tex-math></inline-formula> model can be induced by a Markov model. Then, parameters of each reciprocal <inline-formula><tex-math notation="LaTeX">$CM_L$</tex-math></inline-formula> model can be obtained from those of the Markov model. Also, it is shown that an NG <inline-formula><tex-math notation="LaTeX">$CM_L$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$CM_F$</tex-math></inline-formula>) sequence can be represented by a sum of an NG Markov sequence and an uncorrelated NG vector. This (necessary and sufficient) representation provides a basis for designing parameters of a <inline-formula><tex-math notation="LaTeX">$CM_L$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$CM_F$</tex-math></inline-formula>) model. From the CM viewpoint, a representation is also obtained for NG reciprocal sequences. This representation is simple and reveals an important property of reciprocal sequences. As a result, the significance of studying reciprocal sequences from the CM viewpoint is demonstrated. A full spectrum of dynamic models from a <inline-formula><tex-math notation="LaTeX">$CM_L$</tex-math></inline-formula> model to a reciprocal <inline-formula><tex-math notation="LaTeX">$CM_L$</tex-math></inline-formula> model is also presented. Some examples are presented for illustration.
What It Thinks Is Important Is Important: Robustness Transfers Through Input Gradients

Adversarial perturbations are imperceptible changes to input pixels that can change the prediction of deep learning models. Learned weights of models robust to such perturbations are previously found to be transferable across different tasks but this applies only if the model architecture for the source and target tasks is the same. Input gradients characterize how small changes at each input pixel affect the model output. Using only natural images, we show here that training a student model's input gradients to match those of a robust teacher model can gain robustness close to a strong baseline that is robustly trained from scratch. Through experiments in MNIST, CIFAR-10, CIFAR-100 and Tiny-ImageNet, we show that our proposed method, input gradient adversarial matching, can transfer robustness across different tasks and even across different model architectures. This demonstrates that directly targeting the semantics of input gradients is a feasible way towards adversarial robustness.
Exploratory data analysis for large-scale multiple testing problems and its application in gene expression studies

In large scale multiple testing problems, a two-class empirical Bayes approach can be used to control the false discovery rate (Fdr) for the entire array of hypotheses under study. A sample splitting step is incorporated to modify that approach where one part of the data is used for model fitting and the other part for detecting the significant cases by a screening technique featuring the empirical Bayes mode of Fdr control. Cases with high detection frequency across repeated random sample splits are considered true discoveries. A critical detection frequency is set to control the overall false discovery rate. The proposed method helps to balance out unwanted sources of variation and addresses potential statistical overfitting of the core empirical model by cross-validation through resampling. Further, concurrent detection frequencies are used to provide visual tools to explore the inter-relationship between significant cases. The methodology is illustrated using a microarray data set, RNA-sequencing data set, and several simulation studies. A power analysis is presented to understand the efficiency of the proposed method.
Cross-layer Design and SDR Implementation of DSA, Backpressure Routing and Network Coding

A cross-layer cognitive radio system is designed to support unicast and multicast traffic with integration of dynamic spectrum access (DSA), backpressure algorithm, and network coding for multi-hop networking. The full protocol stack that operates with distributed coordination and local information exchange is implemented with software-defined radios (SDRs) and assessed in a realistic test and evaluation (T\&E) system based on a network emulation testbed. Without a common control channel, each SDR performs neighborhood discovery, spectrum sensing and channel estimation, and executes a distributed extension of backpressure algorithm that optimizes the spectrum utility (that represents link rates and traffic congestion) with joint DSA and routing. The backpressure algorithm is extended to support multicast traffic with network coding deployed over virtual queues (for multicast destinations). In addition to full rank decoding at destinations, rank deficient decoding is also considered to reduce the delay. Cognitive network functionalities are programmed with GNU Radio and Python modules are developed for different layers. USRP radios are used as RF front ends. A wireless network T\&E system is presented to execute emulation tests, where radios communicate with each other through a wireless network emulator that controls physical channels according to path loss, fading, and topology effects. Emulation tests are presented for different topologies to evaluate the throughput, backlog and energy consumption. Results verify the SDR implementation and the joint effect of DSA, backpressure routing and network coding under realistic channel and radio hardware effects.