Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle Sideslip Angle Estimation for a Heavy-Duty Vehicle via Extended Kalman Filter Using a Rational Tyre Model

Vehicle sideslip angle is a key state for lateral vehicle dynamics, but measuring it is expensive and unpractical. Still, knowledge of this state would be really valuable for vehicle control systems aimed at enhancing vehicle safety, to help to reduce worldwide fatal car accidents. This has motivated the research community to investigate techniques to estimate vehicle sideslip angle, which is still a challenging problem. One of the major issues is the need for accurate tyre model parameters, which are difficult to characterise and subject to change during vehicle operation. This paper proposes a new method for estimating vehicle sideslip angle using an Extended Kalman Filter. The main novelties are: i) the tyre behaviour is described using a Rational tyre model whose parameters are estimated and updated online to account for their variation due to e.g. tyre wear and environmental conditions affecting the tyre behaviour; ii) the proposed technique is compared with two other methods available in the literature by means of experimental tests on a heavy-duty vehicle. Results show that: i) the proposed method effectively estimates vehicle sideslip angle with an error limited to 0.5 deg in standard driving conditions, and less than 1 deg for a high-speed run; ii) the tyre parameters are successfully updated online, contributing to outclassing estimation methods based on tyre models that are either excessively simple or with non-varying parameters.
Graph Attention Model Embedded With Multi-Modal Knowledge For Depression Detection

With more than 300 million people depressed worldwide annually, depression is a global problem. The goal of depression detection is to improve diagnostic accuracy and availability, leading to faster intervention. The most important and challenging problem here is to design an effective and robust depression detection model. To this end, there are two challenges to overcome: 1) Multi-modal (audio, image, text, etc.) information must be jointly considered to make accurate inferences. 2) Existing deep learning-based work suffers from multi-modal data sufficiency problem. To address these issues, we propose a graph attention model embedded with multi-modal knowledge for depression detection. This approach learns not only reasonable embeddings for nodes in the knowledge graph, but also exploits medical knowledge to improve the performance of classification and prediction with the knowledge attention mechanism. Experimental results on the real-world datasets show that the proposed approach significantly improves the classification and prediction performance compared with other major state-of-the-art approaches, with guaranteed the robustness with each modality of multi-modal data. Overall, this paper shows how multi-modal knowledge attention mechanism and deep-learning-based networks can be combined to assist mental health patients and practitioners.
PolKA: Polynomial Key-based Architecture for Source Routing in Network Fabrics

Source routing (SR) is a prominent alternative to table-based routing for reducing the number of network states. However, traditional SR approaches, based on Port Switching, still maintain a state in the packet by using a header rewrite operation. The residue number system (RNS) is a promising way of executing fully stateless SR, in which forwarding decisions at core nodes rely on a simple modulo operation over a route label. Nevertheless, such operation over integer arithmetic is not natively supported by commodity network hardware. Thus, we propose a novel RNS-based SR scheme, named PolKA, that explores binary polynomial arithmetic using Galois field (GF) of order 2. We evaluate PolKA in comparison to Port Switching by implementing emulated and hardware prototypes using P4 architecture. Results show that PolKA can achieve equivalent performance, while providing advanced routing features, such as fast failure reaction and agile path migration.
Application of Neural Network Predictive Control in Cement Combined Grinding

The ball mill is a complex industrial controlled object with the characteristics of nonlinearity, pure hysteresis and strong coupling, it's difficult to obtain satisfactory control effect by using conventional control algorithm. The mill system of a cement plant as the research object, a neural network predictive control was proposed to optimize the mill load, the prediction model of mill load was established by BP neural network, the control law was obtained by golden section method, and proposed a feedforward compensation based on expert rules. Simulation results show the effectiveness of neural network predictive control.
Blockchain-Based Controller Recovery in SDN

In this paper, we propose a Blockchain-based solution for the recovery of an SDN controller back to a previously known state upon sudden failure. A lightweight minimal Blockchain ledger containing metadata details about each controller event is maintained by the switches. The set of all instructions given by the controller to the switches denotes the state of the controller at that instant. Whenever a new event occurs, the meta-information about it gets stored in the Blockchain which is updated in the switches after regular epochs. Upon sudden failure and subsequently coming back online again, the controller downloads all the tables and information from the respective switches. It checks and compares the metadata contained in the Blockchain with those data received from the switches. In addition to the existing security services provided by Blockchain, the proposed scheme can further solve the controller failure problem. The performance of the proposed solution is measured through simulation. The proposed scheme with the metadata-based solution saves about 75% of space and a controller can securely recover with a duration of 50 Sec.
Distributed Vehicular Platoon Control with Heterogeneous Communication Delays

Due to its capability of improving fuel economy and traffic efficiency, platooning has become a hot research topic in recent years. Yet platoon internal stability under heterogeneous communication delays is still not fully discussed. This paper proposes an LMI based robust control algorithm for vehicular platoons with generic communication topologies subjected to heterogeneous delays. Inverse vehicle model compensation is utilized to linearize platoon dynamics. Graph theory is employed to depict communication topologies. Together with a linear feedback controller, we yield a high dimensional linear model of the closed-loop platoon dynamics. A Riccati inequality based algorithm is then proposed to calculate a stabilizing control gain, which can be solved in an LMI manner. An upper bound of the heterogeneous communication delay is provided, below which the internal stability of the platoon is theoretically guaranteed. Finally, the proposed theory is validated through numerical simulations.
Solid-State Transformer for Power Distribution Grid Based on a Hybrid Switched-Capacitor LLC-SRC Converter: Analysis, Design, and Experimentation

A solid-state transformer (SST) is being proposed for a distribution grid of 13.8 kV/380 V. The SST is based on the input-series output-parallel (ISOP) arrangement of twelve modules using the 1.2 kV SiC switches. The cost and losses analysis are discussed through a case study regarding the number of modules using the 1.2 kV and 1.7 kV SiC switches. The modules are composed of two stages: the first one is an AC-|AC| low-frequency rectifier and the second one is an |AC|-|AC| medium frequency Hybrid Switched-Capacitor LLC Series-Resonant Converter (HSCSRC). A single back-end |AC|-AC low-frequency inverter is employed to generate the low voltage AC output port of the SST. The switched capacitor ladder cell in the HSCSRC converter enables the reduction of the number of modules employed on the ISOP arrangement because the voltage stresses on the MV side’s switches are halved, while the resonant stage increases the efficiency of the structure due to soft-switching on all the switches. Moreover, the front-end AC-|AC| and back-end |AC|-AC low-frequency converters enable the use of two-quadrant switches on the |AC|-|AC| HSCSRC converter, reducing the switch count on the medium frequency stage. The operating behavior and design methodology of the SST’s modules are presented. A single module reduced scale prototype with the rated power of 1.67 kVA and an input voltage of 1.15 kV and an output voltage of 220 V is experimentally verified. The maximum efficiency is 97%.
A 29.2 Mb/mm2 Ultra High Density SRAM Macro using 7nm FinFET Technology with Dual-Edge Driven Wordline/Bitline and Write/Read-Assist Circuit

A 29.2Mb/mm2 ultra high density SRAM macro has been proposed using 7-nm CMOS FinFET technology. The SRAM macro has only one SRAM cell array despite of the huge array of 512 rows × 512 columns. The circuitry of dual-edge driver for such long wordline and bitline in such huge array are newly proposed. The SRAM macro using proposed circuit was designed, and a test chip was fabricated using 7-nm CMOS FinFET technology. The minimum operation voltage is improved 170 mV by the new circuits.
Improving the Quality of Intermittent Wind Power and its Harvest for Steam Power Plant Operation on Power Systems

Wind power generation exhibits intermittence on the power produced which constitutes a disadvantage to its integration into the power system operation. The challenges of handling wind power intermittence on the power system are enormous, as the impact requires the use of additional generation resources on the conventional system in order to improve the performance of the system. In this work, a remedy to the intermittence and improvement of the wind power harvest is proposed. The wind power is generated using modified wind turbines and the power is stored on a heat transfer fluid. The stored heat is then used to produce steam which is processed in a heat exchanger to drive a steam turbine power plant. This eliminates the disturbance due to the wind intermittence as presented on the results, where the power system exhibits a more stable operation (higher minimum singular values) when using the heat storage as compared to the direct wind power injection and also the wind power harvest is improved by using modified wind turbines.
MPC Control method of vehicle longitudinal collision avoidance based on hierarchical control

In this paper, a hierarchical control method is proposed to design the vehicle longitudinal collision avoidance controller. After establishing the vehicle inverse longitudinal dynamics model, the upper layer acceleration model predictive controller and the underlying single neuron PID controller are designed respectively. The simulation results show that the controller can successfully avoid collision and satisfy the safety, stability and comfort.
Hierarchical Tracking Control With Arbitrary Task Dimensions: Application to Trajectory Tracking on Submanifolds

Hierarchical impedance control has been recently shown to effectively allow trajectory tracking, while guaranteeing the order of priorities during the execution. Nevertheless, the choice of the tasks is required to be such that, after being properly decoupled, they are all feasible and lead to an invertible Jacobian matrix. In this work, a modification is proposed that removes both these restrictions. The user is free to specify as many tasks as desired and especially without necessarily guaranteeing in advance that none of the tasks will become singular during the execution. Whenever tasks with higher priority use-up all the degrees of freedom, all the other tasks are naturally ignored. Still, as soon as some of the tasks with higher priority become singular, then the freed-up controllability is used to execute the next task in the stack. This is realized automatically, without any rearrangement of the tasks in the priority stack. As an application, the case of trajectory tracking on a submanifold of the workspace is considered, in which multiple charts of the atlas are used for the tasks. Simulations are used to validate the stability analysis.
Raptor-like Rateless Spinal Codes using Outer Systematic Polar Codes for Reliable Deep Space Communications

Deep space communications have characteristics such as long delay, easily broken link and high code error rate. In this paper, a Raptor-like rateless spinal coding scheme is proposed by concatenating the outer systematic polar code (SPC), so as to promote the error-correction performance for the deep space communications. An interleaving operation is used to arrange the inner coding order of SPC bits. Moreover, a segmented cyclic redundancy check (CRC)-aided decoding approach is exploited to reduce the complexity of the concatenating decoding. The rateless transmission control is designed accordingly. Numerical simulation results demonstrate the performance advantage of the proposed polar-spinal codes compared to the existing rateless codes for the reliability acquisition of deep space communications.
Modeling and Identification of an Industrial Hot Water Boiler

One of the most important open issues in industrial automation is a nonlinear dynamical system identification. In the following paper, the grey and black box approaches are implemented for hot water boiler modeling and identification. First-principles approach is applied to the hot water boiler modeling, and symbolic regression is performed for identification of the same industrial process. Then, the implementation of both methods is described. The resulting models are validated and compared on the basis of performance metrics. Finally, the encountered problems in the realization of the methods are reviewed.
Deep Level Set with Confidence Map and Boundary Loss for Medical Image Segmentation

Level set method is widely used for image segmentation. Recent work combined traditional level set method with deep learning architecture for image segmentation. However, it is limited when dealing with medical images because of the blurred edges and complex intensity distribution, which leads to the loss of spatial details. To address this problem, we propose a deep level set method to refine object boundary details and improve the segmentation accuracy. We integrate augmented prior knowledge into inputs of CNN, which can make the level set evolution result has more accurate shape. In addition, to consider the spatial correlation of the object, we combine a boundary loss with deep level set model for preventing the reduction of details. We evaluate the proposed method on two medical image data sets, which are prostate magnetic resonance images and retinal fundus images. The experimental results show that the proposed method achieves state-of-the-art performance.
Approaches for the Prediction of Lead Times in an Engineer to Order Environment—A Systematic Review

The interest of manufacturing companies in a sufficient prediction of lead times is continuously increasing - especially in engineer to order environments with typically a large number of individual parts and complex production processes. A multitude of approaches have been proposed in the literature for predicting lead times considering different data and methods or algorithms from operations research (OR) and machine learning (ML). In order to provide guidance at setting up prediction models and developing new approaches, a systematic review of the available approaches for predicting lead times is presented in this paper. Forty-two publications were analyzed and synthetized: Based on a developed framework considering the used data class (e.g. product data or system status), the data origin (master data or real data) and the used method and algorithm from OR and ML, the publications are classified. Based on the classification, a descriptive analysis is performed to identify common approaches in the existing literature as well as implications for further research. One result is, that mostly order data and the status of the production system are used for predicting lead times whereas material data are used seldom. Additionally, ML approaches primarily use artificial neural networks and regression models for predicting lead times, while OR approaches use mainly combinatorial optimization or heuristics. Furthermore, with increasing model complexity the use of real data decreased. Thus, we identified as an implication for further research to set up a complex data model considering material data, which uses real data as data origin.
Analysis Of The Efficiency Of The Adaptive Canny Method For The Detection Of Icebergs At Open Sea

ICWG The detection of icebergs in the open sea, as well as its evolution in displacement and shrinking, is vital for navigation, the study of the evolution of Polar regions, and the Earth climate change, among others. In order to carry out these studies, it is necessary to delimit accurately the icebergs in satellite images, mainly of the Synthetic Aperture Radar (SAR) type. The Adaptive Canny method has shown to be efficient for the detection of edges of objects in SAR images, according to recent publications and conferences. These studies were only carried out for images that had approximately half of each backscatter, without considering that the dimension of the objects can affect the edge detection process. Here, we present the results of the efficiency of the Adaptive Canny method as the size of the object, from which it is intended to extract the contour, decreases. A systematic analysis of the behavior of the method has been performed with objects of variated dimensions, through a Monte Carlo type experiment with synthetic images, where the contours of the figures were extracted with the Adaptive Canny method and compared with the Ground Truth (GT). Then, the method was tested on real images of the Antarctic Ocean, with blocks of ice of different sizes to contrast the results with those obtained with synthetic images.
Research on Voltage Control of Distribution Network based on Constraint Programming

At present, with the access of new power sources and loads such as small-capacity distributed power supply and electric vehicles, on the one hand, the traditional passive distribution network is gradually transformed into an active distribution network, changing the one-way flow mode of the power flow of the traditional radiant distribution network, which makes the voltage distribution of the distribution network more complicated. On the other hand, the randomness and fuzziness of the distributed power supply and EV charging load make the load prediction of the distribution system more difficult and increase the difficulty of the voltage management of the distribution system. In this paper, the reactive power output of distributed power supply, charge and discharge power of energy storage device and flexible controllable load are controlled to optimize the voltage distribution and operating network loss of distribution network under new energy environment. Firstly, a stochastic chance constrained programming model for reactive voltage coordination optimization of distribution network under new energy environment is established, and the rationality and effectiveness of the model is verified by taking the improved IEEE33 node as an example.
A 108-Gb/s 64-QAM CMOS D-Band Rx With Integrated LO Generation

In this letter, an ultrabroadband D-band wireless front-end receiver (RX) in 45-nm CMOS SOI is proposed. It achieves a noise figure of 8 dB, a gain of 16 dB, a 3-dB bandwidth of 25 GHz, and occupies a compact area of 2 mm<sup>2</sup>. To the best of our knowledge, for the first time, the required millimeter-wave local oscillator signal is generated on-chip. Furthermore, 108-Gb/s data-rate is demonstrated by receiving and demodulating a signal composed of <inline-formula> <tex-math notation="LaTeX">$10\times 2.16$ </tex-math></inline-formula> GHz channels with −40-dBm average power and 64-QAM modulation. With a consumption of 200 mW from a 1-V voltage supply, the proposed RX achieves an energy efficiency of 1.8 pJ/bit.
Overview of Edge Computing in the Agricultural Internet of Things: Key Technologies, Applications, Challenges

The application of the Internet of Things in agricultural development usually occurs via a monitoring network that consists of a large number of sensor nodes, thus gradually transforming agriculture from a human-oriented and single-machine-centric production model to an information- and software-centric production model. Due to the large area coverage of agriculture and the variety of production objects, if all farmland perception information is gathered into the cloud server, the server will exert greater pressure on the network, which reduces the speed of response to event processing. This problem may be perfectly solved by the recent emergence of Edge computing, which can share the load of the cloud server and reduce the delay. Edge computing has prospects in agricultural applications, such as pest identification, safety traceability of agricultural products, unmanned agricultural machinery, agricultural technology promotion, and intelligent management. The application of the Agricultural Internet of Things integrates artificial intelligence, the Internet of Things, and blockchain and Virtual/Augmented Reality technologies. This paper primarily reviews the application of Edge computing in the Agricultural Internet of Things and investigates the combination of Edge computing and Artificial Intelligence, blockchain and Virtual/Augmented reality technology. The challenges of Edge computing task allocation, data processing, privacy protection and security, and service stability in agriculture are reviewed. The future development direction of Edge computing in the Agricultural Internet of Things is predicted.
Multi-CNN Feature Fusion for Efficient EEG Classification

Recently, motor imagery (MI) signals have been used in BCI systems for disabled persons, controlling robots or wheelchairs or even driving cars. Hence, researchers are using machine learning and deep learning techniques for decoding MI signals. The properties of EEG signals like a low signal to noise ratio and its dynamic nature make it more complex and harder to decode. Research has shown that EEG has both spatial and temporal characteristics which can be exploited by deep learning models like convolution neural networks (CNN). This paper shows that the multilevel CNN model can extract dynamic correlations from EEG MI data. Multilevel CNN models are fused using autoencoder to extract the best features for EEG data which help improve MI decoding accuracy. The proposed fusion methods give a good performance for EEG decoding when tested for within-subject trials as well as cross trials. The results are compared with state-of-the art-techniques on public EEG dataset BCI competition IV 2a. Novel cross encoding technique is also proposed which helps us achieve big improvement in cross-subject EEG decoding.