Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Selection Based on the Kullback-Leibler Distance and its Application on Fault Diagnosis

The core concept of pattern recognition is that digs inner mode between data in the same class. The within-class data has a similar distribution, while between-class data has some distinction in different forms. Feature selection utilizes the difference between two-class data to reduce the number of features in the training models. A large amount of feature selection methods have widely used in different fields. This paper proposes a novel feature selection method based on the Kullback-Leibler distance which measures the distance of distribution between two features. For fault diagnosis, the proposed feature selection method is combined with support vector machine to improve its performance. Experimental results validate the effectiveness and superior of the proposed feature selection method, and the proposed diagnosis model can increase the detection rate in chemistry process.
Mapping and Localization using Semantic Road Marking with Centimeter-level Accuracy in Indoor Parking Lots

Accurate localization is one of the fundamental tasks of vehicles visual navigation in parking lots. In this paper, we propose a practical and novel solution, which exploits road marking semantic segmentation to attack the problem of long-term and high-precision visual localization. Based on the semantic data association derived from road markings segmentation, point cloud fusion and loop detection strategies are designed to improve the performance of semantic map building. Applying the generated map, we present a point cloud registration algorithm combining semantic and geometric inference to improve the localization precision. Experiments on real-world indoor parking lots prove that the semantic map created by the proposed method reveals more accurate and consistent performance. Moreover, localization error is no more than 10cm, while running in real-time performance.
Attention Prediction on Webpage Images using Multilabel Classification

Image elements (called webpage images) are prominent in drawing the user attention on webpages. Quantitatively predicting the attention on webpage images help in their synthesis as well as rendering. This paper presents the attention prediction as a multilabel classification problem for the webpage images. Firstly, fixated images were assigned multiple quantified visual attention (called fixation-indices) based on users’ sequential attention allocation on webpages. Subsequently, fixated images’ visual features such as intensity, brightness, color histograms, position and size on respective webpages, were extracted. A multilabel classification problem formulated using the visual features and fixation-indices was solved using multiple binary support vector machine (binary-SVM) classifiers. The performance of the proposed approach was analyzed through a free-viewing eye-tracking experiment conducted on 36 real-world webpages with 42 participants. Our model outperforms the baseline with precision, recall, Fl-score, and an accuracy of 84.14%, 78.91%, 76.57%, 67.49% respectively with a hamming loss of 23.62% and a subset 0/1 loss of 66.50%.
Optical flow-based segmentation of moving objects for mobile robot navigation using pre-trained deep learning models*

The segmentation of moving objects from video sequences is an intensely researched topic. The two leading application domains are video surveillance systems and self-driving vehicles. Many of the current approaches use some deep learning architecture to accomplish the task. For the training of such models, an extensive collection of input data is necessary. However, it is hard to find an openly accessible, properly annotated dataset for this task in the field of mobile robotics, so one has to create their own. Deep learning models of high complexity easily overfit small datasets if they are trained from scratch, therefore readily available pre-trained networks should be better used. The pretrained deep learning models usually expect RGB images as their input, meanwhile most of the state-of-the-art approaches for moving object segmentation incorporate optical flow data in the prediction process as well. In this paper, we propose a moving object segmentation deep neural network for avoiding dynamic obstacles in the case of indoor mobile robot navigation. The network was built on the top of a deep learning model that was pre-trained for RGB image recognition. We encode the optical flow data and the video frames together into a format that can be processed by the pre-trained network. We introduce a compound loss and training strategy for the model. The empirical evaluation results show that our network predicts satisfactory segmentation masks on the test data for the task of dynamic obstacle avoidance in indoor mobile robot navigation while operating in real-time ($\sim 30$ fps) and being robust to camera movement and the appearance of moving objects.
Design and Optimization of Synchronous Motors for Low-Voltage Electric Vehicles

For a given overload capability and flux-weakening speed range an effective procedure to design low-voltage synchronous motors is described. It considers the inverter Volt-Ampere rating, that is, voltage and current limits. Some examples are reported. After a preliminary analytical design, finite element analysis is adopted and linked to a multi-objective optimization. Finally, a prototype is manufactured and tested to validate the predicted results.
Switching Losses Reduction of Grid-tied Inverters With Variable Switching Frequency Discontinuous PWM

Three-phase two-level inverters are widely used in industry for dc-ac power conversion. Discontinuous pulse width modulation (DPWM) can reduce power losses but cause worse current quality. A same switching frequency is often applied for three phases in conventional modulation methods. This paper proposes a variable switching frequency discontinuous PWM method by increasing the switching frequency near the current zero-crossing point. Thus, better harmonic performance and negligible losses increase can be achieved. The discussion of the deadtime and narrow pulse effect is also presented and a simple carrier-based algorithm is proposed to implement this method. Simulation and experimental results are presented to verify the performance of the proposed method.
Deep Sea TDOA Localization Method Based on Improved OMP Algorithm

Compressed sensing is recently applied to time delay estimation, resulting in higher accuracy and stability compared to traditional methods. In this paper, a time delay estimation model is designed based on adaptive iterative local searching orthogonal matching pursuit (AILSOMP) algorithm, and an improved three-stage weighted least squares localization algorithm is proposed using the time delay values. Firstly, the sensor receives acoustic waves from the target in the deep-sea multipath environment. It then obtains the rectilinear propagation time delay of the sound wave through compressed sensing. Secondly, the time synchronization between the two sensors is maintained, and the difference between the estimated delays of both sensors is multiplied by the speed of sound to obtain the measured distance value. Finally, an improved three-stage weighted least squares algorithm is applied to locate the target using the time difference of arrival (TDOA). Simulation results confirm that the proposed algorithm has better localization performance compared to other methods in a multipath interference environment.
Parallel Loading and Unloading: Smart Technology towards Intelligent Logistics

The ACP theory plays an important role in modeling and controlling complex, uncertain systems. Parallel loading and unloading is the specific application of parallel theory in the field of parcel handling. This paper proposes the basic framework of the parallel loading and unloading system. Under this framework, the physical loading and unloading process is described by constructing a artificial loading and unloading system. On this basis, the artificial system predicts the best loading and unloading solution through computational experiments. The physical loading and unloading system executes the solution from the artificial system. Finally, the parallel execution builds a bridge between the physical system and the artificial system. The parallel system will be updated and optimized continuously. Parallel loading and unloading is one smart technology toward intelligent logistics, which provides a new methodology for parcel handling technology.
Measurement of important circuit parasitics for switching transient analysis of SiC MOSFET and Schottky diode pair

Characterization of external circuit parasitics are important to study the switching dynamics and adverse effects related to that. This paper presents a set of simple experimental measurement techniques to determine different external circuit parasitics relevant for switching transient study of Silicon Carbide (SiC) MOSFET and Schottky barrier diode (SBD) pair. The simulation and experimental results confirm the accuracy of the presented method over a range of operating conditions for a 1.2-kV discrete SiC MOSFET and SBD pair.
A study to design the locations of reversible traction substations for minimizing power losses of DC railways

With the increasing concern about the energy and environmental sustainability, energy reduction for railway systems is becoming more significant. This paper evaluates the typical electrification scheme of DC-fed railway systems and proposes a new scheme with inverters for DC railways. With inverters, the regenerated energy by braking trains can be transferred to the distribution network. The design of the locations of inverting substations for minimising power losses is optimised based on a DC railway line.
GaN E-HEMT based High Flatness Broadband Power Noise Source for Dynamic Identification of Mechatronic Systems and Batteries

Still today the dynamic identification of mechatronic systems - in particular electrical machines - is often based on stimulating them with deterministic signals, being single sinusoidal tones, single steps or periodical pulse trains of fixed frequency the most common ones. Such methodologies were the most, when not the only, practicable ones when the instrumentation was limited to low frequency generators and analog oscilloscopes, at most. The modern metrological scenario allows to concretize way more sophisticated approaches nowadays, which can bring to Mechatronics more holistic and easily executable identification methods; like those based on flat noise sources traditionally used in Radio Frequency (RF). More in general, the adoption of SiC and GaN fully controllable switches demands that also Power Electronics become eventually permeated with the very different way of thinking peculiar of RF. Within the framework of such cultural and methodological evolution that Power Electronics must embrace, the paper describes the unconventional use of converters based on GaN switches as powerful high-flatness noise sources for broadband dynamic identification, here tailored for modern electrical machines and battery technologies. Indeed, owing to the much shorter raise and fall times, as well as much higher switching frequencies enabled by GaN, it is possible to greatly extend the upper boundaries of the frequency bands where desired spectra can be synthesized by commutating such fully controllable switches according to proper time-based patterns. The authors illustrate the use of a randomly PWM-modulated H-bridge employing GaN E-HEMTs as high-flatness noise source up to at least 200 kHz thanks to 1 MHz PWM carrier. Various selected experimental results from identifications of BLDC Motors and Lithium-Iron-Phosphate $(LiFePo_{4})$ cells are shown. The details in the figures are fully visible by magnifying the electronic version of the paper, which represents the same approach when Microfilms were widely used.
High-Frequency RCS Estimation

Radar Cross Section (RCS) is one of the necessary factors for the design of communication system antenna and aircraft, where the scattering and reflection of electromagnetic waves plays a vital role. To measure RCS in electrically large objects such as aircraft and ships large amounts of resources are required. In this paper, we used the free function and TLS (Total Least Squares) theory to low-frequency RCS data and predicted high-frequency RCS data. In this approach, the result shows that the numerical verification of estimates lies within the tolerance limit.
Robotic Walker with High Maneuverability through Deep Learning for Sensor Fusion

Traditional robotic walkers have primarily focused on safety and navigation. In this paper, we challenge the previous work on walkers by implementing a deep learning module developed with the goal of using a robot to provide mobility assistance to the elders. Through the data collected from multiple sensors, we are capable of leveraging the maneuverability of robotic walkers under different scenarios and gait requirements. This capability is achieved by CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) architectures. Thus, the system can provide personalized assistance to the elders performing the locomotion activities indoors accurately. Furthermore, the robot learns the optimal behavior based on the interactions with the environment in a supervised learning approach. To validate our system, we evaluated the system with some users who provided qualitative comments about the comfort degree of using the robot.
Variable Admittance Control based on Virtual Fixtures for Human-Robot Co-Manipulation

Cobots (Collaborative robots) are increasingly employed in industrial workplaces, assisting human operators in decreasing the weight and the repetitiveness of their activities. In order to promote the acceptance of cobots, novel interaction paradigms enabling intuitive collaboration between humans and robots are needed. In this work, we propose a variable admittance control framework based on virtual fixtures, in which the damping and the stiffness of the admittance controller are on-line adjusted to increase the effectiveness of the co-manipulation task. Virtual paths are generated to support and guide the operator during collaborative task execution, providing him/her an over-responsive compliant system when the robot is far from the targets and a precise heavy tool in their proximity. The proposed approach has been compared with a fixed admittance controller in an industrial use case consisting of a human operator interacting with a Kuka LBR IIWA arm. The collected results demonstrate the effectiveness of the proposed approach.
Virtual Transformer Control for DC-DC Interlinking Converters in DC Microgrids

In a DC microgrid, a converter connecting two DC buses is a DC-DC interlinking converter (ILC). In the ILCs, this paper proposes a simple control solution to handle two control issues simultaneously: a) high-voltage-side-series low-voltage-side-parallel (HSLP) modular ILC, b) control adaptability in different grid-working modes. Existing closed-loop control methods are complex and usually do not consider the two issues together. Open-loop controls feature very low cost for realization. Some open-loop controlled ILCs show good performances in the two issues: automatic voltage sharing in HSLP modules, good adaptability in different grid-working modes. The secret is that in these open-loop controlled ILCs: when the steady converter power changes, steady voltages of the two ports are nearly proportional. However, open-loop controls are restricted to certain converter topologies and they cannot modify the converter performances based on sampling feedback. For DC-DC ILCs, this paper proposes a very simple but comprehensive closed-loop control named ‘virtual transformer’. This control uses closed-loop feedback to learn the proportional-port-voltage feature from the open-loop controlled ILCs. Thus these good performances and the low cost are inherited, while the main shortcomings of open-loop controls are overcome.
Simulation of Transmission Grid Operation Incorporating Flexibility at Distribution Level

The ongoing structural changes in the power system are characterized by an increasing share of renewable energy sources, energy storages and demand flexibility in distribution grids. This is associated with growing interactions between transmission and distribution system operators and requires mechanisms to coordinate and exchange flexibility for providing ancillary services. This contribution analyzes the impact of flexibility of generation and storages at distribution level on the congestion management in the transmission grid. For this purpose, distributed flexibility is aggregated at high-voltage level and modeled as a separate energy storage system equivalent at each high-voltage node. The storage system equivalents allow temporal power shifts and are integrated in a congestion management simulation considering further grid-and market-related measures. The model is exemplarily showcased for a future scenario of the German energy system with a detailed representation of subordinate grid structures to analyze the impact of distributed flexibility on the congestion management in large-scale transmission systems. The results illustrate the high potential of distributed flexibility to contribute to congestion management and partially substitute conventional redispatch measures and the curtailment of onshore and offshore wind parks.
Robust Localization with Low-Mounted Multiple LiDARs in Urban Environments

In this paper, we propose a robust, real-time and scalable localization framework for multi-LiDAR equipped vehicles in challenging urban environments. Our test vehicle uses multiple LiDARs with low mounting positions which makes the localization task very challenging in dense traffic scenarios due to the reduced field-of-view of LiDARs and additional uncertainty introduced by the dynamic vehicles. In order to increase the robustness and provide consistently smooth localization output, LiDAR localization is fused with the dead reckoning using the probabilistic scan matching confidence estimation method. We conducted experiments in different urban settings and results confirmed that our framework can operate reliably with low-position multi-LiDAR suite under various traffic scenarios.
Dynamic Surveillance by Multiple Agents with Fuel Constraints

The surveillance problem is to find optimal trajectories of agents that patrol a given area as evenly as possible. In this paper, we consider multiple agents with fuel constraints. The surveillance area is given by a weighted directed graph, where the weight assigned to each arc corresponds to the fuel consumption/supply. For each node, the penalty to evaluate the unattended time is introduced. Penalties, agents, and fuels are modeled by a mixed logical dynamical system model. Then, the surveillance problem is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, the MILP problem is solved at each discrete time. In this paper, the feasibility condition for the MILP problem is derived. Finally, the proposed method is demonstrated by a numerical example.
Learning Word Embeddings Using Spatial Information

This study proposes a word embedding learning method for intelligent document search which can consider the meanings of place-related named entity word. In the previous method, the word embeddings were learned based on the context in which the words emerged. However, because the previous method missed the spatial information, the word embeddings that share similar contexts (e.g., universities) become similar even though the entities of the words were different (e.g., “東京大学” (The University of Tokyo) and “京都大学” (Kyoto University)). In the proposed method, the word embeddings are learned based on the spatial information data, which comprise both the object names and coordinates. Therefore, even if the words share similar contexts, the proposed method can learn different word embeddings for different words if those entities are different. The proposed method is evaluated using the synonym search task. As a result, it was observed that the mean reciprocal rank (MRR) for the evaluation data improved using the proposed method in comparison with the previous method. Furthermore, the proposed method improved the MRR by 177% maximally, i.e., from 0.151 to 0.418, in comparison with the previous method.
Formation of Hump Effect Due to Top-Gate Bias Stress in Organic Thin-Film Transistors

This study investigated the reliability of top-gate p-type organic thin-film transistors in vacuum under positive bias stress-induced and positive bias illumination stress-induced instability degradation. The manufacturing process suggested that sidewall dielectric insulating layers are thin. In addition, a shorter organic gate dielectric sidewall causes a larger electric field. Therefore, sidewall electron traps exhibit parasitic transistor characteristics, and the parasitic channel experiences premature conduction, triggering an abnormal hump phenomenon. The mechanism of degradation is verified through electric field simulation; this mechanism is generated owing to the bias stress of the gate. These observations indicate that organic thin-film transistors should be designed with a suitable sidewall insulation thickness to reduce the influence of the sidewall electric field.
Crossing-Road Pedestrian Trajectory Prediction via Encoder-Decoder LSTM

In urban road scenarios with coexistence of vehicles and pedestrians, the ability of predicting pedestrians’ future position is essential for the intelligent vehicle to avoid potential collision risk and make reasonable path planning. For vehicles and pedestrians, the behaviors and states of both sides will affect each other to make their judgments of "right of way". However, most of the previous works have ignored the interaction characteristic of traffic participants in the pedestrian trajectory prediction task. which could hardly describe the interaction scenario. We proposed a novel network architecture based on the encoder-decoder Long Short-Term Memory (LSTM) network. A double-channel encoder is designed to extract the state streams from both vehicle trajectory and pedestrian trajectory. Then the state fusion is implemented in the decoder to generate the future trajectory of pedestrian. In experiments, our method has been compared with both Dynamical Motion Models based method and data-driven based method. The results verified the effectiveness of our method especially on the Daimler dataset and a new established dataset VPI. The results verified the effectiveness of our method especially in long term prediction.
Discrete-time SMO based Sensorless Control of CSC-fed PMSM Drives with Low Switching Frequency

In this paper, current source converter (CSC)-fed permanent magnet synchronous motor (PMSM) drives are investigated for Electrical submersible pump (ESP) applications. The CSC features motor-friendly waveform with low dv/dt, which is very suitable for long cable application. High speed ESP by adopting PMSM has higher efficiency, shorter length and diameter compared with the counterpart which adopting induction motor (IM). Sensorless control instead of mechanical sensor can further reduce the system size/cost and improve reliability. However, low switching frequency and inverter-side output filter cause great challenge in controller and observer design. These kinds of influence become more serious under higher speed region due to the limited updates per fundamental cycle. To overcome these challenges, the dynamic capacitor voltage control (DVC) is employed in field-oriented control (FOC) scheme to enhance the system dynamic response. Moreover, a discrete-time sliding mode observer (SMO) based rotor position estimation method is proposed to reduce estimation error and improve parameter uncertainty robustness. The effectiveness of the proposed method is verified on a permanent synchronous machine (PMSM) with simulation and experiment results.