Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Route with Sparse Trajectory Sets

Motivated by the increasing availability of vehicle trajectory data, we propose learn-to-route, a comprehensive trajectory-based routing solution. Specifically, we first construct a graph-like structure from trajectories as the routing infrastructure. Second, we enable trajectory-based routing given an arbitrary (source, destination) pair. In the first step, given a road network and a collection of trajectories, we propose a trajectory-based clustering method that identifies regions in a road network. If a pair of regions are connected by trajectories, we maintain the paths used by these trajectories and learn a routing preference for travel between the regions. As trajectories are skewed and sparse, %and although the introduction of regions serves to consolidate the sparse data, many region pairs are not connected by trajectories. We thus transfer routing preferences from region pairs with sufficient trajectories to such region pairs and then use the transferred preferences to identify paths between the regions. In the second step, we exploit the above graph-like structure to achieve a comprehensive trajectory-based routing solution. Empirical studies with two substantial trajectory data sets offer insight into the proposed solution, indicating that it is practical. A comparison with a leading routing service offers evidence that the paper's proposal is able to enhance routing quality.
Investigating Pole Assignment of Full-Order Observer Based on Extended Electromotive Force for Position Sensorless Control of IPMSMs

Several position sensorless control methods of interior permanent magnet synchronous motors (IPMSMs) using observers have been proposed. In the case of full-order observers, these designs are difficult because the order of the error equations of observers becomes an order of four. In this paper, to realize the position sensorless control of IPMSMs, a design method for full-order observers based on the extended electromotive force (EEMF) is proposed, and the pole assignment of the proposed observer is studied. Experimental results of the position sensorless control of an IPMSM show the usefulness of this proposed design method for the full-order observer compared to the case where poles are merely assigned on the left half plane.
The case for experiment-oriented computing

Experimentation aspects (e.g., systematic observation, exploration of alternatives, formulation of hypotheses and empirical testing) can be found dispersed and intertwined in many software systems. Numerous examples are provided in this article. This suggests that experimental activity is a class of computation in its own right. By abstracting the relevant experimentation features, a general Experiment-Oriented Computing (EOC) approach, orthogonal to other Software Engineering issues, is formulated in this article. Through this separation of concerns, it is possible to clearly pursue both theoretical and applied research with respect to experimental aspects. Concrete directions for such research and development are also given to illustrate the value of the approach.
Sonothrombolysis - Dissolving Thrombi by Interaction of the Drug and Ultrasound

Under the influence of pathological changes, the blood coagulates inside the blood vessel, creating a thrombus. The thrombus dissolution process is called thrombolysis. The aim of the study is to evaluate the thrombolysis process by the interaction of the thrombolitic drug and ultrasound. The clot dissolution process was analyzed in a specially designed, transparent for ultrasound parallel plate flow chamber. Inside, a freshly coagulated human blood sample was exposed to ultrasound. A liquid containing the tissue plasminogen activator drug in a concentration of 10 μg/ml passed around the sample. The liquid flow was forced by a peristaltic pump. The source of ultrasound was a 1 MHz flat ultrasonic transducer with a 25 mm diameter. The transducer radiated a 1000 periods burst, repeated every 2500 periods and space averaged time averaged intensities of 0.2-1.6 W /cm2, The efficacy of thrombus dissolution was observed by means of a designed parallel plate flow chamber and the time of thrombus complete dissolution was measured. The best result for the 1 MHz frequency and space averaged time averaged intensity of 1.6 W/cm2 was recorded, where the thrombus was dissolved within 5.5 minutes.
MVDC Distribution Grids for Electric Vehicle Fast-Charging Infrastructure

The electrification of the mobility sector is steadily increasing. Besides established electrical vehicles like railways, also the number of full-electric busses with on-board batteries grows. Moreover, the acceptance and hence the share of electric cars in traffic is continously rising. To further accelerate the electrification of public and individual traffic, large-scale usual and fast-charging infrastructures should be developed. This paper presents different approaches to ensure the proper integration into existing electrical grids. Also, the power supply with medium-voltage direct-current (MVDC) distribution grids that lowers the costs for loss energy and components is presented.
Automatic Pyramid Evaluation Exploiting EDU-based Extractive Reference Summaries

This paper tackles automation of the pyramid method, a reliable manual evaluation framework. To construct a pyramid, we transform human-made reference summaries into extractive reference summaries that consist of Elementary Discourse Units (EDUs) obtained from source documents and then weight every EDU by counting the number of extractive reference summaries that contain the EDU. A summary is scored by the correspondences between EDUs in the summary and those in the pyramid. Experiments on DUC and TAC data sets show that our methods strongly correlate with various manual evaluations.
eMIMIC: Estimating HTTP-Based Video QoE Metrics from Encrypted Network Traffic

Understanding the user-perceived Quality of Experience (QoE) of HTTP-based video has become critical for content providers, distributors, and network operators. For network operators, monitoring QoE is challenging due to lack of access to video streaming applications, user devices, or servers. Thus, network operators need to rely on the network traffic to infer key metrics that influence video QoE. Furthermore, with content providers increasingly encrypting the network traffic, the task of QoE inference from passive measurements has become even more challenging. In this paper, we present a methodology called eMIMIC that uses passive network measurements to estimate key video QoE metrics for encrypted HTTP-based Adaptive Streaming (HAS) sessions. eMIMIC uses packet headers from network traffic to model a HAS session and estimate video QoE metrics such as average bitrate and re-buffering ratio. We evaluate our methodology using network traces from a variety of realistic conditions and ground truth of two popular video streaming services collected using a lab testbed. eMIMIC estimates re-buffering ratio within 1 percentage point of ground truth for up to 70% sessions and average bitrate with error under 100 kbps for up to 80% sessions. We also compare eMIMIC with recently proposed machine learning-based QoE estimation methodology. We show that eMIMIC can predict average bitrate with 2.8%-3.2% higher accuracy and re-buffering ratio with 9.8%-24.8% higher accuracy without requiring any training on ground truth QoE metrics.
Audio Signal and Troubleshooting System Based on Wireless Sensor

<p class="0abstract"><span lang="EN-US">To explore</span><span lang="EN-US"> the application of audio signal in the troubleshooting system, a sound detection system </span><span lang="EN-US">wa</span><span lang="EN-US">s designed. The system consists of three parts: voice acquisition node, aggregation node and host computer monitoring software. Time protocol synchronization </span><span lang="EN-US">(</span><span lang="EN-US">TPSN</span><span lang="EN-US">)</span><span lang="EN-US"> algorithm </span><span lang="EN-US">wa</span><span lang="EN-US">s used to realize the synchronization between nodes. The algorithm and the trilateration method </span><span lang="EN-US">we</span><span lang="EN-US">re applied to the system. The application</span><span lang="EN-US">s</span><span lang="EN-US"> of the sound detection system in the field of fault sound source localization </span><span lang="EN-US">we</span><span lang="EN-US">re realized. </span><span lang="EN-US">Wireless sensor networks </span><span lang="EN-US">we</span><span lang="EN-US">re used in sound detection systems, which ha</span><span lang="EN-US">d</span><span lang="EN-US"> many advantages. On the one hand, complicated wiring </span><span lang="EN-US">wa</span><span lang="EN-US">s avoided. It ha</span><span lang="EN-US">d</span><span lang="EN-US"> the advantages of easy to set up and easy to move. </span><span lang="EN-US">On the other hand, some self-organizing and adaptive features in wireless sensor networks and some methods of synchronization and localization c</span><span lang="EN-US">ould</span><span lang="EN-US"> be introduced. The</span><span lang="EN-US">whole system</span><span lang="EN-US"> was</span><span lang="EN-US"> more flexible</span><span lang="EN-US"> and i</span><span lang="EN-US">ts application </span><span lang="EN-US">wa</span><span lang="EN-US">s more extensive. </span><span lang="EN-US">Using monitoring software, users can remotely access to the scene of the voice signal. </span><span lang="EN-US">The results show</span><span lang="EN-US">ed</span><span lang="EN-US"> that the system ha</span><span lang="EN-US">d</span><span lang="EN-US"> high transmission rate, stable operation and small positioning error. Therefore, it has good application prospects. </span></p>
Transient Stability Impact of the Phase-Locked Loop on Grid-Connected Voltage Source Converters

The phase-locked loop (PLL) is widely used in the grid-connected voltage source converter (VSC) for the purpose of grid synchronization. The impact of the PLL on the small-signal stability of VSC has recently been revealed, yet its influence on the transient stability of the VSC has seldom been addressed. This paper thus presents a comprehensive analysis on the transient stability effect of the PLL. It points out that the conventional equal-area criterion (EAC), which is used to analyze the transient stability of the synchronous generator (SG), cannot be extended to analyze the PLL effect, even though a dynamic analogy between the PLL and the swing equation of the SG can be found. The phase portrait is thus used in this work, and it shows that the long settling time and a high damping ratio of the PLL can enhance the transient stability of the VSC. Finally, the experimental tests are performed to verify the effectiveness of the theoretical analysis.
Two-Dimensional Analysis for Conical Permanent Magnet Motors

Conical motors are nowadays finding a niche place in certain aspects of industry. Due to their geometrical features and the particular flux paths, they intrinsically need complex 3D finite-element tools for accurate analysis. In this paper, however a more cost-effective but still highly accurate modelling methodology for conical machines is proposed and validated. The methodology is based on the development of an equivalent 2D process. This paper first describes the basic attraction forces between rotor and stator of a conical machine, then explains the concept of the proposed methodology, investigates it thoroughly with finite element analysis and finally validates the whole concept against experimental results achieved on a purposely-built prototype. All this clearly indicates the validity of the proposed method and process.
View Selection for Virtual View Synthesis in Free Navigation Systems

The paper deals with the problem of the optimal selection of the real views used for the virtual view synthesis in the free navigation systems. The practical free navigation system requires the real-time view synthesis. Therefore, the virtual view cannot be synthesized using the information from all the real cameras, but only few, most proper ones. In the paper the method of fast selection of two proper real views is presented. The proposed approach allows to select the real views for different camera arrangements, thus can be applied to the practical free navigation systems.
Supporting Set Operations in Temporal-Probabilistic Databases

In temporal-probabilistic (TP) databases, the combination of the temporal and the probabilistic dimension adds significant overhead to the computation of set operations. Although set queries are guaranteed to yield linearly sized output relations, all of the existing solutions exhibit a quadratic runtime complexity. They suffer from redundant interval comparisons and additional joins for the formation of lineage expressions. In this paper, we formally define TP set operations and study their properties. For their efficient computation, we introduce the lineage-aware temporal window, a mechanism that binds intervals with lineage expressions. We suggest the lineage-aware window advancer (LAWA) for producing lineage-aware temporal windows, which enable direct filtering of irrelevant intervals and finalization of output lineage expressions. This way, we compute TP set operations in linearithmic time. A series of experiments over both synthetic and real-world datasets show that (a) our approach has predictable performance, which depends only on the size of the input relations and not on the number of time intervals per fact or the overlap of the time intervals, and that (b) it outperforms state-of-the-art approaches.
A High Power LED Supply Based on GaN Synchronous Boost Converter

In this paper we present the design of digitally controlled 150 W synchronous boost converter with application as high power LED driver. Usage of GaN FETs provides higher efficiency compared with traditional solutions incorporating MOSFET transistors. Design claims 12 V input voltage and output up to 35 V. Due to relatively high frequency (310 kHz), it achieves good performance at small dimensions. Digital regulator built with STM32F334 microcontroller allows easy control of output power by voltage or current stabilization. The converter can be controlled by digital encoder or serial interface.
Analyzing the News Coverage of Personalized Newspapers

Traditionally, news media organizations used to publish only a few editions of the printed newspapers, and all subscribers of a particular edition used to receive the same information broadcasted by the media organization. The advent of personalized news recommendations has completely changed this simpler news landscape. Such recommendations effectively produce numerous personalized editions of a single newspaper, consisting of only the stories recommended to a particular reader. Although prior works have considered news coverage of different newspapers, due to the difficulty of knowing what news is recommended to whom, there has been no prior study to look into the coverage of information in different personalized news editions. Moreover, the evolution of the effects of personalization on recommended news stories is also not explored. In this work, we make the first attempt to investigate these issues. By collecting extensive data from New York Times personalized recommendations, we compare the information coverage in different personalized editions and investigate how they evolve over time. We observe that the coverage of news stories recommended to different readers are considerably different, and these differences further change with time. We believe that our work will be an important addition to the growing literature on algorithmic auditing and transparency.
Degree-based Outlier Detection within IP Traffic Modelled as a Link Stream

This paper aims at precisely detecting and identifying anomalous events in IP traffic. To this end, we adopt the link stream formalism which properly captures temporal and structural features of the data. Within this framework, we focus on finding anomalous behaviours with respect to the degree of IP addresses over time. Due to diversity in IP profiles, this feature is typically distributed heterogeneously, preventing us to directly find anomalies. To deal with this challenge, we design a method to detect outliers as well as precisely identify their cause in a sequence of similar heterogeneous distributions. We apply it to several MAWI captures of IP traffic and we show that it succeeds in detecting relevant patterns in terms of anomalous network activity.
A Control Strategy of MMC Battery Energy Storage System Based on Arm Current Control

A control strategy of MMC battery energy storage system(MMC-BESS), which is based on arm current control, is proposed in this paper. Compared with other strategies, there are three technological merits of this strategy. First of all, it could control arm current directly to achieve triplex-control which covers Ac-side current, Dc-bus current and circulating current, without the specific circulating control strategy. In addition, compared with grid side reference current, it could easily give the signal of arm reference current without conversion of coordinates and numerous computations. At last, this strategy realizes a distributed balance control of the capacitance voltage of each sub-module through hierarchical control, and it also can actualize bidirectional flow of active power between any two ends of Dc-side, Ac-side and sub-module battery side. Simulation experiment based on PSIM verifies the effectiveness of this strategy.
Mackerel Classification Using Global and Local Features

Blue and chub mackerels are often caught at the same time, and their market prices are different. Thus, humans need to classify them by their hands. Therefore, the demand for automatic sorting machines of these mackerels using image processing technique is increasing. Classification for blue and chub mackerels is a challenging task due to their quite similar appearance. In this paper, we propose a method for classifying blue and chub mackerels by image processing technique. The difference of the two mackerels appears on the whole and the local specific parts. Therefore, we use global and local features that are extracted from the whole and the specific part in fish images, respectively. Experimental results showed that the proposed method is superior to the methods using only global or local feature.
Fault Tolerant Predictive Control of Three-Level Neutral-Point-Clamped Back-to-Back Power Converters

This paper presents a fault diagnosis and fault-tolerant control method for open-circuit fault in three-level neutral-point-clamped back-to-back power converters. The proposed fault diagnosis method is based on analysis of probability distribution of stator current. No extra sensor is needed. Additionally, fault detections for both one- and two-phase open-circuit fault are achieved. The proposed fault-tolerant control scheme is a non-redundant solution and is embedded into the well-known model predictive control (MPC) frame. Simulation results validate the feasibility and effectiveness of the proposed fault diagnosis and fault-tolerant control methods.
Supervised and Unsupervised Methods for Robust Separation of Section Titles and Prose Text in Web Documents

The text in many web documents is organized into a hierarchy of section titles and corresponding prose content, a structure which provides potentially exploitable information on discourse structure and topicality. However, this organization is generally discarded during text collection, and collecting it is not straightforward: the same visual organization can be implemented in a myriad of different ways in the underlying HTML. To remedy this, we present a flexible system for automatically extracting the hierarchical section titles and prose organization of web documents irrespective of differences in HTML representation. This system uses features from syntax, semantics, discourse and markup to build two models which classify HTML text into section titles and prose text. When tested on three different domains of web text, our domain-independent system achieves an overall precision of 0.82 and a recall of 0.98. The domain-dependent variation produces very high precision (0.99) at the expense of recall (0.75). These results exhibit a robust level of accuracy suitable for enhancing question answering, information extraction, and summarization.
Visual Object Recognition and Pose Estimation Based on a Deep Semantic Segmentation Network

In recent years, deep learning-based object recognition algorithms become emerging in robotic vision applications. This paper addresses the design of a novel deep learning-based visual object recognition and pose estimation system for a robot manipulator to handle random object picking tasks. The proposed visual control system consists of a visual perception module, an object pose estimation module, a data argumentation module, and a robot manipulator controller. The visual perception module combines deep convolution neural networks (CNNs) and a fully connected conditional random field layer to realize an image semantic segmentation function, which can provide stable and accurate object classification results in cluttered environments. The object pose estimation module implements a model-based pose estimation method to estimate the 3D pose of the target for picking control. In addition, the proposed data argumentation module automatically generates training data for training the deep CNN. Experimental results show that the proposed scene segmentation method used in the data argumentation module reaches a high accuracy rate of 97.10% on average, which is higher than other state-of-the-art segment methods. Moreover, with the proposed data argumentation module, the visual perception module reaches an accuracy rate over than 80% and 72% in the case of detecting and recognizing one object and three objects, respectively. In addition, the proposed model-based pose estimation method provides accurate 3D pose estimation results. The average translation and rotation errors in the three axes are all smaller than 0.52 cm and 3.95 degrees, respectively. These advantages make the proposed visual control system suitable for applications of random object picking and manipulation.
Software- Defined Networking as an Enabler for Future Industrial Network Management

The overall Industry 4.0 (I4.0) developments combined with the disruptive process of IT-based digitalisation create a vast amount of new opportunities but also challenges for the industrial automation domain. The combination of hybrid (wired & wireless) communication architectures, already widely installed legacy technologies, new approaches, such as Time-Sensitive Networking (TSN) or 5G, and the general heterogeneity of the industrial landscape results in a high configuration complexity. This creates the necessity for future-proof industrial communication network management systems. Therefore, this paper summarises the current state of the art in this area in order to identify the specific requirements towards future industrial network management systems. The most promising candidate is the Software-Defined Networking (SDN) concept. To evaluate SDN as a possible enabler, specified industrial requirements are compared with the current technological and conceptual capabilities of SDN. In addition, drawbacks resulting in future research questions are identified.
A Bi-Directional Current Detection Using Current Transformers for Bi-Directional DC-DC Converter

This paper proposes a bi-directional current detection circuit using current-transformers for bi-directional DC-DC converters. This current detection circuit consists of CT(Current transformer) and electric components. The features of the current sensor based on CT can detect a pulse current contain DC component without any magnetic saturation. This paper shows a circuit configuration of the bi-directional current sensor and the principle of detection error of this circuit is analyzed. The validity of this analysis is verified through experiment under the DC-DC converter rated at 1.4 kW.