Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer Learning with PipNet: For Automated Visual Analysis of Piping Design

This paper presents an end-to-end learning approach based on latest CNN architectures and transfer learning to perform vision-based analysis of engineering designs. The specific application considered here is the design of pipe networks on-board ships or offshore platforms. Having a piping design in the form of an image, a framework known as Piping Net (PipNet) is introduced to understand the design and interpret if it complies with applicable engineering regulations. Designs and corresponding labels (compliant or non-compliant) are fed into an existing trained CNN in the form of images for transfer learning, with the subsequently obtained fine-tuned network called PipNet. Based on Regulation 12, Annex I Regulations for the Prevention of Pollution by Oil, International Convention for the Prevention of Pollution from Ships (MARPOL) and Rules for Classification of Ships of Lloyd's Register, two datasets containing 3,234 piping designs in the form of images were used for performance evaluation. The developed system demonstrates outstanding performance on these two challenging datasets.
A Combined Thermal Analysis of an Integrated Six-Phase Motor Drive System

This paper presents a combined thermal model for analyzing the thermal behavior of a six-phase modular integrated motor drive system (IMDS). The proposed thermal model is established based on the thermal resistance network (TRN) considering the physical integration of the motor and its drive. The temperatures of the key components, such as the motor winding, magnet and the drive power modules, can be predicted simultaneously by the proposed thermal model. It shows the integration results in a serious thermal coupling between the motor and drive, and has to be carefully dealt with at the design stage to avoid the possible derating or overheating. An IMDS prototype have been built. DC tests on the motor have been implemented to validate the motor TRN at present.
Draft Genome Sequence of “Candidatus Bathyarchaeota” Archaeon BE326-BA-RLH, an Uncultured Denitrifier and Putative Anaerobic Methanotroph from South Africa’s Deep Continental Biosphere

Metagenomic sequencing of fracture fluid from South Africa recovered a nearly complete “Candidatus Bathyarchaeota” archaeon genome. The metagenome-assembled genome of BE326-BA-RLH contains genes involved in methane metabolism and dissimilatory nitrate reduction. ABSTRACT Metagenomic sequencing of fracture fluid from South Africa recovered a nearly complete “Candidatus Bathyarchaeota” archaeon genome. The metagenome-assembled genome of BE326-BA-RLH contains genes involved in methane metabolism and dissimilatory nitrate reduction. This study presents the first genomic evidence for potential anaerobic methane oxidation in the phylum “Ca. Bathyarchaeota. ”
An Auxiliary-Parallel-Inductor-Based Sequence Switching Control to Improve the Load Transient Response of Buck Converters

In this paper, a sequence switching control (SSC) scheme based on an auxiliary parallel inductor is proposed to improve the transient performance of buck converters. The proposed SSC scheme introduces a small controlled inductor in parallel with the output inductor, so as to increase the inductor-current slew rate when it is activated during a load transient. Furthermore, an “<italic>n</italic> + 1” sequence switching strategy is proposed to control the auxiliary parallel inductor. The proposed scheme divides the transient event into <italic>n</italic> + 1 periods. In the former <italic>n</italic> periods, the same small output voltage deviations are designed, while in the last period, a smaller deviation is done for a smooth transition from the transient to the steady state. The switching sequence is derived applying the capacitor–charge balance principle for each of <italic>n</italic> + 1 periods. For a given buck converter in the experiment, the settling time and the output voltage deviation of the proposed SSC scheme enhance more than 50% and 67%, respectively, over those of the time-optimal control for a 3-A load step.
High Quality Voltage Regulation of Single Phase Autonomous Microgrids Under Nonlinear Load Conditions

Conventionally, multiple Distributed Generation systems operating as a standalone microgrid use droop control concepts to match load demand and share power. However, it is challenging to maintain a high quality microgrid voltage using droop control alone, particularly when nonlinear loads and load transients are considered. Recently, a resonant voltage regulation system integrated with a synchronous reference frame virtual impedance loop and a phase locked loop, has been shown to achieve significantly improved performance both in terms of power sharing and dynamic response. This paper now applies this approach to a single phase microgrid system operating with significant levels of nonlinear loads, showing how similar performance benefits can be achieved in this particularly difficult operating context. The advantages of the approach are shown by detailed simulation and experimental studies across a variety of load conditions.
Dual-Band Bandpass Filter Using Varactor Loaded Square Split Ring Resonator

A dual-band bandpass filter (BPF) using varactor-loaded square split ring resonator is proposed to realize the dual-band frequency response and one high-selective tunable passband. This resonator is mainly composed of a pair of the square split ring, two varactors and an open stub $(l=\lambda/4@f_{even})$. By placing an open stub at the center position of the proposed resonator, the odd-mode resonance $f_{\text{odd}}$ is split into two modes, as $f_{\text{odd}1}$ and $f_{odd2}$, which can be used to form a high-selectivity passband. The two distinct odd/even-mode resonances can be generated independently and used to realize the first-/second-passbands. The simulated results indicate that the center frequency of the first-passband can be tuned from 1.15 to 1.45 GHz with a 3-dB constant bandwidth of $110\pm 10 \ \text{MHz}$. meanwhile, the center frequency of the second-passband is fixed at 2.2 GHz with a 3-dB fractional bandwidth of 8.6-11.4%.
Visual Analytics for Decomposing Temporal Event Series of Production Lines

The temporal analysis of events in a production line helps manufacturing experts get a better understanding of the line’s performance and provides ideas for improvement. Especially the identification of recurring error patterns is important, because these patterns can be an indicator of systematic production issues. We present a visual analytics approach to analyze event reports of a production line. Reported events are shown as a time series plot that can be decomposed into a trend, seasonal, and remainder component by applying Seasonal Trend decomposition using Loess (STL). To find specific event patterns, the data is filtered based on aspects such as the event description or the processed product. Identified temporal patterns can be extracted from the original event series and compared visually with each other. In addition to predefined settings, experts can define a subseries of the event series and the period length of STL’s seasonal component through an automatically optimized brushing of the undecomposed plot. We developed the approach together with an industry partner. To evaluate our approach, we conducted two pair analytics sessions with our industry partner’s experts. We demonstrate use cases from these sessions that showcase our approach’s analytical potential. Moreover, we present general expert feedback that we collected through semi-structured interviews after the pair analytics sessions.
A SIW-Based Wideband Tapered Slot Antenna (TSA) Array for MMW Applications

In this paper, a wideband tapered slot antenna (TSA) array using substrate integrated waveguide (SIW) technology is proposed for millimeter wave (MMW) applications. Rectangular corrugation structures are used to reduce side lobe level (SLL) and improve antenna gain. A pair of semicircular notches are inserted to TSA element to expand impedance bandwidth by adding surface current path. Then, a 4-element TSA array is designed and fabricated. Simulated and measured results obtain great agreement. The measured operating band is 33-46.8 GHz (relative bandwidth is 34.5 %) for return loss $< -10\mathrm{dB}$. Besides, gain curve has a stable trend at operating band and the maximum gain is 16 dBi at 46 GHz.
Zero-Voltage Switching Control of a Grid-Connected Interleaved Inverter with Variable Switching Frequency

Interleaved technique is widely applied in ac-dc, dc-dc, and dc-ac converters to improve the dynamic response, efficiency and power density. While a multi-phase interleaved converter reduces the total output current ripple, each interleaved phase can operate in the high current ripple (HCR) mode to minimize the inductance. This paper proposed a variable switching frequency control in a grid-connected interleaved inverter to keep a stable valley current with the opposite polarity to the average current, so to achieve the ZVS mode. The proposed approach realizes the ZVS operation at full ranges of a grid-connected application, and can be easily implemented in a digital signal controller. In addition, the effectiveness of the ripple cancellation is investigated at variable switching frequency. The effectiveness and performance of the proposed control methods is validated by experiments.
Harmonics Suppression and Control Design for General-Purpose Mission Profile Emulator of Three-Phase Power Electronics System

The quantified reliability estimation of power electronics system is growing more and more crucial. To enable dedicated tests and validation for the power electronics designs in respect to the reliability and functionality, a general-purpose mission profile emulator, which only consumes the power losses of the circuits and can emulate the operating conditions of multiple three-phase DC-AC power electronics applications, is proposed. However, in this mission profile emulator, there exists zero-sequence current path which is different from most three-phase converter systems, resulting in distorted emulation effects especially in respect to the low-order harmonic characteristics. Design and comparison of several control/circuit solutions for the mission profile emulator is conducted on the aspect of harmonic suppression capability, and the computation time for each solution is also evaluated because enough computation capability of controller has to be reserved for other tests and control functions. Experimental validation is also conducted to verify the conclusions throughout the analysis.
Evaluation of the Effectiveness of the Visual Navigation Technique for an Autonomous Underwater Vehicle

This paper considers an approach to a method for navigation of autonomous underwater vehicle based on visual odometry with respect to the conditions of the local maneuvers. Some techniques for evaluation of several modifications for the AUV visual navigation method, previously developed by the authors are described. The results of modeling and water pool experiments for considered algorithms are supplemented.
Nonlinear control of multibody systems in shape space

Nonlinear control of planar multibody systems motivated by the classical cat-fall problem and the more practical problem of reorientation of multibody satellites in space are studied. A multibody system model reduced by translational and rotational symmetries was assumed in a Hamiltonian setting. A further reduction by the first integral (the system angular momentum) results in a configuration space of relative joint angles. This is equivalent to reducing the system to the symplectic leaf of the previously assumed model. The system after reduction is still Hamiltonian, and a canonical representation can be obtained. Angular-momentum-preserving controls generated by joint motors were introduced. The application of this linearizing input results in a reduced-dimension model and was found to capture the dynamics of the system in the shape space. The state space was extended to track the change in phase shift of the absolute angles. An important reachability result is proved. An optimal control problem was formulated to accomplish reorientation. <<ETX>>
Exploring the Cross-Domain Action Recognition Problem by Deep Feature Learning and Cross-Domain Learning

Action recognition has received increasing attention from the computer vision and machine learning communities in the last decade. Although many related action recognition algorithms have been proposed, similar environments conditions are often required in the training and testing stages, which limits the application of the related technologies. In order to accelerate the generalization of action recognition, in this paper, the cross-domain action recognition problem are explored by three different kinds of aspects: 1) feature learning, hand-crafted feature and deep learning feature are extracted, respectively, and then the generalization ability of them are assessed and discussed on controlled and uncontrolled environments, respectively; 2) unsupervised cross-domain learning, since it is difficult for us to obtain the labeled samples in the target domain, thus, unsupervised cross-domain learning methods can be borrowed. In order to discuss which one is suitable for open domain action recognition problem, thus, three kind of unsupervised cross-domain learning methods are assessed on open domain action recognition dataset, respectively; 3) supervised cross-domain learning, if there are some labeled samples in the target domain, but the number of them is very limited, thus, supervised cross-domain learning method should be a good choice, but, how do we make the decision for them? Therefore, these methods are also appraised on the same dataset. Moreover, we contribute a novel multi-view and multi-modality human action recognition dataset (abbreviated as ” $MMA$ ”). It consists of 7,080 action samples from 25 action categories, including 15 single-subject actions and 10 double-subject interactive actions in three views of two different scenarios, which can be utilized to simultaneously explore single-view learning, multi-view learning, multi-modality learning, and cross-domain learning problems. We further explore the same learning problems on the MMA dataset. The extensive experimental results on two different datasets show that the deep feature learning method has much better generalization ability than the hand-crafted feature, such as improved dense trajectory if there are enough labeled samples in the training dataset to be used to fine-tune the network, and both unsupervised cross-domain learning method and supervised cross-domain learning method can improve the performance, but the latter can obtain much bigger improvement, in other words, the labeled samples in the target domain are very helpful. Finally, we also attended the open domain action recognition challenge which was held in CVPR 2017 workshop, and our supervised cross-domain learning scheme obtained the best performance in all teams.
Audio processing with using Python language science libraries

The topic of the article is recognition of instruments and playing techniques of music for detection and correction of errors in a given music sample. It shows how to achieve characteristics of recorded sound and also how to compare amplitudes and frequencies of the same music piece, but played by different persons and also with using various instruments. For this aim the signal processing algorithms are used, which are available in standard Python libraries such as “numpy” or “scipy”. The key idea of the processing is detection of errors, but save playing technique and individual style of the player.
Design and Use of the Callflow Module in Executable Choreographies for IoT

The Internet of Things (IoT) is an ecosystem of computer devices, people or animals which hold unique identifiers and have the ability to transfer data over the network without involving human-to-human or human-to-computer interaction. Over the years, there have been a number of challenges in building a scalable IoT system. These challenges include asynchronous communication, for which we propose a solution developed within the PrivateSky project [1], based on the representation of the asynchronous code as a set of phases in a workflow, which we will present in Section 4.
Fast and Efficient Aerial Climbing of Vertical Surfaces Using Fixed-Wing UAVs

We present improvements to Sherbrooke's multimodal autonomous drone (S-MAD), a microspine-based perching fixed-wing UAV that enables thrust-assisted climbing along vertical surfaces. Aircraft models are used to predict the performance of various aerial climb regimes and to design a controller for wall distance tracking. It is found that fast, long, and vertical climbs are favorable. Both short and long vertical autonomous climb maneuvers are demonstrated on rough surfaces (e.g., brick, roofing shingles). Results show that the S-MAD compares favorably with existing climbers, reaching a specific resistance of 19 with a much faster vertical speed (i.e., 2 m/s). A reduction in S-MAD's aerodynamic drag and an improved motor efficiency could bring its specific resistance down to 7, at a vertical speed of 5 m/s.
A Practice to Search the Summit of a DEM Using Simulated Annealing Technique

Simulated annealing (SA) algorithm is based on iterative improvement but with stochastic acceptance of bad moves. This allows the algorithm to escape local optimal solution in early sampling rounds and the progressive refinement into efficient solutions in later sampling rounds. In this paper, we show how this algorithm is used to find the highest peak of a Digital Elevation Model (DEM). In order to search efficiently in the terrain data, we proposed a reformed cost function which used logarithmic transformation. A set of parameters were confirmed after many experiments. The result shows that SA is not guarantee to find the optimal solution. However, there are 16% chances to find the highest peak during 400 experiments. Specifically, the search process is demonstrated by 3D visualization in GIS, which can help understand the mechanism of the algorithm and provide convenience for teaching.
A Step Toward Learning to Control Tens of Optically Actuated Microrobots in Three Dimensions

Automated manipulation of a large number of independently controlled microrobots in three dimensions can potentially lead to a major breakthrough in the formation of functional microstructures, both engineering and biological, in a scalable and reliable manner. In this paper, we provide the first foundational step toward realizing this objective by developing a reinforcement learning-based model predictive controller. Our controller (approximately) optimizes the trajectories of the microrobots through importance sampling, while ensuring that the trajectories do not cause collisions with workspace obstacles, such as other randomly dispersed microobjects or already-formed microstructures. Simulation experiments with tens of microspheres, actuated using holographic optical traps, demonstrate the feasibility and usefulness of our method.
Data Offloading for Deadline-Varying Tasks in Mobile Edge Computing

Opportunistic mobile networks are efficient complementary forms of data transmission in Mobile Edge Computing, which consist of many mobile users and could reduce the costs. In this paper, we focus on offloading deadline-varying tasks, mainly depending on the contact frequency and durations. Different from existing work, we consider multi-tasks and the influence of contact situation on probabilities. However, the problem is NP-hard. Thus, we design the offline data offloading heuristic algorithm to schedule a series of tasks to encountering mobile nodes or Wi-Fi APs. To fully utilize the real-time contact information, we further propose the online offloading algorithm, which adjusts the offloading scheme depending on the new contact parameters. Finally, we conduct simulations and evaluate the performance of our algorithms, comparing to other algorithms. Moreover, we demonstrate that our algorithms perform better than the compared algorithms through detailed comparison.
An iterative multiple sampling method for intrusion detection

ABSTRACT Threats to network security increase with growing volumes and velocity of data across networks, and they present challenges not only to law enforcement agencies, but to businesses, families and individuals. The volume, velocity and veracity of shared data across networks entail accurate and reliable automated tools for filtering out useful from malicious, noisy or irrelevant data. While data mining and machine learning techniques have widely been adopted within the network security community, challenges and gaps in knowledge extraction from data have remained due to insufficient data sources on attacks on which to test the algorithms accuracy and reliability. We propose a data-flow adaptive approach to intrusion detection based on high-dimensional cyber-attacks data. The algorithm repeatedly takes random samples from an inherently bi-modal, high-dimensional dataset of 82,332 observations on 25 numeric and two categorical variables. Its main idea is to capture subtle information resulting from reduced data dimension of a large number of malicious flows and by iteratively estimating roles played by individual variables in construction of key components. Data visualization and numerical results provide a clear separation of a set of variables associated with attack types and show that component-dominating parameters are crucial in monitoring future attacks.
Draft Genome Sequences for a Diverse Set of Seven Haemophilus and Aggregatibacter Species

Haemophilus is a complex genus that includes commensal and pathogenic species that pose a public health threat to humans. While the pathogenic species have been studied extensively, many commensals have limited genomic information available. ABSTRACT Haemophilus is a complex genus that includes commensal and pathogenic species that pose a public health threat to humans. While the pathogenic species have been studied extensively, many commensals have limited genomic information available. Here, we present 24 draft genomes for a diverse set of 7 Haemophilus and Aggregatibacter species.
Path Planning of Multi-Modal Underwater Vehicle for Adaptive Sampling Using Delaunay Spatial Partition-Ant Colony Optimization

Multi-modal underwater vehicle (MUV) is a newly ocean observation platform with the capability to switch between Argo mode and glider mode. This paper presents an efficient and effective path planner to generate trajectory that exploits ocean energy and makes use of favorable ocean currents to propel and lead the MUV to collect ocean measurements located in high scientific interest areas. The proposed strategy employs MUV mode transition within path planner to enable efficient operation of MUV to drift with ocean currents for measurements and transform to operate as an underwater glider for locomotion. The novel hybrid scheme is the first to integrate tournament selection based Delaunay triangulation into ant colony optimization (ACO), herein referred to as DSP-ACO path planner to facilitate more efficient searching for adaptive ocean sampling problem (AOSP). Simulation results show that the proposed DSP-ACO path planner is capable to take advantage of ocean currents and obtain a more optimal trajectory with quicker convergence rate than conventional ACO path planner. Meanwhile, MUV transforms operation between Argo mode and glider mode is more energy efficient to take measurements than underwater glider. Monte Carlo simulation is run to assess the robustness of the DSP-ACO path planner and demonstrates the inherent superiority of the proposed DSP-ACO path planner.