Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Map Calculation of a Salient-Pole Synchronous Motor with Hairpin Winding

Leveraged by the increasing interest in electrical drives for automotive applications, variable speed drives for vehicle traction have gained more attention recently. However, this industry sector sets challenging demands on the manufacturing process of electric machines regarding scalability, repeatability and total manufacturing time. Hairpin windings are a promising option to meet these requirements. However, since hairpin windings are usually characterized by the use of large solid conductors and a low number of turns per coil, eddy-current effects should be considered. In automotive applications, the operating point an electric motor works on varies considerably. Thus, it is advisable to assess the machine’s performance for a range of possible operating points. Performance maps give a clear overview of physical quantities of an electric drive for different steady-state torque-speed operating points. This work will give insight into a workflow developed to calculate performance maps for quantities such as ohmic losses and total efficiency as functions of the demanded operating point numerically. A salient-pole, wound-rotor synchronous machine is chosen as an example traction machine in the analyses. The workflow is used to evaluate the frequency-dependent losses for different points of operation.
Determination of Bone Density and Bone Composition Using Thermoaravimetric Analysis

Surgically obtained cancellous bone samples from the human osteoarthritic femoral heads (n=10) were used to measure bone mineral density (BMD) and the total amount of organic components. BMD was determined by thermogravimetric analysis (TGA), dual X-ray absorptiometry (DXA) and ashing. Furthermore, bone composition (amount of inorganic versus organic components) was analyzed with TGA. The measurement data showed a high linear correlation between TGA and DXA with r = 0.82 (p < 0.01) and between TGA and ashing with r = 0.83 (p < 0.01). Comparison of bone density values from DXA versus ashing revealed a correlation coefficient of r= 0.81 (p < 0.01). Between 200°C and 600 °C, a reduction of the bone sample mass of about 50 % was measured with TGA due to loss of organic components. TGA demonstrates its suitability to analyze bone mineral density and bone composition of samples derived from osteoarthritic patients.
Adaptive Gain Control for a Steady Human-Robot Cooperation

In this paper, a force control is adopted to help an operator manipulate a robotic arm directly and efficiently in a path planning task. To achieve this, we mount a 6-DOF force sensor on the tip of the robotic arm. The operator can manipulate the robotic arm easily by pushing and pulling the force sensor. However, the main problem faced in this work is when the robot is pulled to a target by the operator, its end-effector will contact with surroundings with the result that the contact force repels the end-effector from the target. Thus, the robot attempts to attain the target position since there is an error between its current and target positions. Previous studies have shown that conservative control can suppress the rebounding effect, but maintaining it will cause the operator to suffer excessive resistance in subsequent operations. To solve this problem, we propose a collision rate to detect any collision by analyzing force behavior. Then, we use this rate to adjust the control gain of robot behavior. The proposed method was validated on an industrial robotic arm. The result shows that the robot produced a small resistance for manual manipulation and avoided bouncing behavior when touching any surroundings.
Position Control and Vibration Suppression for Flexible-Joint Surgical Robot

To achieve vibration suppression and precise position control for minimally invasive surgical robot during intraoperative operation, a compound control method composed of an input shaper and a pole-placement predictive function controller (PPFC) is proposed. First, the flexible-joint dynamic model of the 3-degree of freedom remote center motion (RCM) mechanisms of minimally invasive surgery robot is linearized by using the gravity compensation, and then the linearized dynamic model is decomposed into four parallel first-order sub-models. Next, PPFC is used to realize the position tracking of the robotic arm in the movement, and ZV input shaping is used to suppress the residual vibration of flexible-joint manipulator after stopping motion. Finally, the comparative experiments are conducted to evaluate the performance of the proposed method.
Mobile Phone Spam Text Classification Based on Prior Knowledge

Due to the rapid development of the Internet, the data generated by people in life is growing at an exponential rate, and Short Message Service (SMS) data is one of the social media products of mobile phone users. The main discussion direction of this article is how to distinguish spam messages and obtain effective information from them, and distinguish the information expressed by the messages themselves. In recent years, Deep learning has made great breakthroughs in the field of textual classification of natural language processing, so this paper will do more research and breakthrough on spam texts using the deep learning method. This article will introduce a Model (RCM) combined with a priori information extracted by Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). Firstly, obtain the temporal and spatial information characteristics of the sentence using the bidirectional LSTM as the prior information of the information itself before reading the information, and then merge with the feature information processed by the CNN to improve the accuracy of the spam prediction. Compared with the previous model, there is some innovation in getting the priori information extracted by LSTM, and it is also improve the performance standard.
Unleash Narrowband Technologies for Industrial Internet of Things Services

As the industrial market grows, it is becoming noticeable that there are many industrial Internet of things (IIoT) use cases for which existing technology cannot meet the huge demand of machine connectivity. For example, in the utility market, there is a strong trend to adopt new technology that can support positive business use case scenarios for efficient system operation and elaborate the dramatic increase of the services demands. Apart from this, most utility grid applications required long-range, low-power, secure, and reliable communications, which means narrowband (NB) technology can be the dominant choice. To address these challenges, this article provides a new framework architecture to enable technical decision makers to plan for NB-IIoT. Moreover, we highlight the key aspects of NB technology by focusing on the challenges, standardization, and requirements to facilitate the IIoT connectivity for industry revolutions. The motivation behind employing NB is to provide a high level of reliability, and better quality of service, and coverage. In particular, the article addresses the main applications of utility use cases under the NB umbrella, which can perform as a good bridge between utility services and the fundamental communication infrastructure. The utility use cases based on emerging technology can support the full array of smart grid services that are required for both central and distributed operation systems. Finally, the article provides connectivity solutions for potential IIoT deployment aiming to define a new roadmap for NB technology on specific industrial use cases.
Quantitative Formula of Blink Rates-Pupillometry for Attention Level Detection in Supervised Machine Learning

Attention is a condition when someone concentrates on a specific task while ignoring other perceivable information. Numerous methods of attention level detection such as observation, self-assessment, and objective performance have been applied especially in supervised machine learning. But those methods tend to be delayed, sporadic, not at the moment in time, and based on participant cognitive ability. This study proposed a new labeling method for attention level detection by using quantitative evaluation formula based on blink rates and pupillometry. Comparison in error detection between self-assessment, observation, and objective performance has been done in this study. After that, this study investigated the effect of attention level based on self-assessment toward blink rates and pupillometry. The result shown blink rates in low attention is higher than high attention. On the other hand, pupillometry in low attention is smaller than high attention. The effect of attention levels toward pupillometry and blink rates are extracted into several algorithms. The result from experimental procedure shown quantitative evaluation formula has percentage error less than 15% compared with self-assessment. Overall, these results demonstrated that the proposed method can be used to be data labeling for other physiological signals such as electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS). After that, this quantitative formula was applied to EEG–ECG–NIRS for attention level detection. Two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS has been used to detect attention level during tasks load. Our result has shown the accuracy system 82.31%.
ABC Algorithm Based Multiuser Detector for TDRSS Forward Link MA Service

Forward link of tracking and data relay satellite system (TDRSS) can offer multiple access (MA) service for several spacecraft simultaneously by using code-division multiple-access (CDMA). This work focuses on the multiuser detection problem for TDRSS forward link MA service. An efficient heuristic method-artificial bee colony (ABC) algorithm-is employed to solve the known NP-hard problem. The discretization of ABC algorithm is explored to employ ABC algorithm in multiuser detection problem. Besides, the output of matched filter and its variations are employed as initial values for artificial bees to accelerate the convergence of ABC algorithm based multiuser detection. The simulation results show that the bit error ratio (BER) and near-far effect resistance performances of the proposed detector are quite close to the optimal multiuser detector with reasonable computational complexity. Furthermore, the proposed ABC algorithm based multiuser detector also has a large system capacity.
Design of ∆Σ Based PID Controller for Wind Energy Systems

The present study designs a novel ∆Σ-based PID controller for controlling the rotor side converter (RSC) of a doubly-fed induction generator (DFIG) based wind energy conversion system (WECS). The proposed controller essentially uses ∆Σ modulators to represent the analogue signals in single-bit which is ideally suited for FPGA or application specific integrated circuit implementation and offers numerous advantages due to its inherent parallel nature. The performance of the controller is compared with various controllers including a conventional PID, Q and Dynamic Fuzzy-Q learning (DFQL) based intelligent controllers. The results of the simulation on WECS consists of 20 wind turbines and 20 DFIGs, illustrate that the ∆Σ based PID controller could perform better compared to conventional PID and Q-learning based controllers.
EMB-SLAM: An Embedded Efficient Implementation of Rao-Blackwellized Particle Filter Based SLAM

Simultaneous localization and mapping (SLAM) algorithms are an essential component for autonomous mobile robotics to be able to operate in a priori unknown environments. In the last two decades, plenty of SLAM algorithms have been developed and a number of optimizations have been done for those algorithms. However, rarely optimization approaches to low-cost and energy-efficient embedded systems that are suitable for indoor robotics have been done. The benefit of the development of embedded systems should be explored. With the emerging of new technologies (multi core, ARM® NEON™) which can greatly accelerate the processing speed, rethinking the implementation of algorithms should be done. In this work, a new embedded efficient Rao-Blackwellized particle filter based Simultaneous Mapping and Localization (EMB-SLAM) implementation is presented. It is based on the co-design with the multi-core embedded hardware, a SLAM algorithm and an optimization methodology. EMB-SLAM is tested with real datasets. Experiments show the real-time performance of this implementation, and demonstrate that the embedded system is suitable for realizing SLAM applications under real time constraints.
The Optical Subsystem for the Empty Containers Recognition and Sorting in a Reverse Vending Machine

A reverse vending machine (RVM) is a machine where people can return empty beverage containers like plastic bottles and cans for recycling taking back a deposit or refund amount. Reverse vending machines are a key part of container deposit systems in Europe and the United States. Waste recognition and sorting in RVM machines can be performed by any of the following procedures: by determining the container material (e.g. using the IR-spectrometer), by recognition of the container type by its shape, or by the barcode identification. These three basic control-procedures make any attempt of the fraud completely impossible. But at the same time, it makes the RVM too expensive. With the modern computer vision technologies, we can design another kind of efficient and non-expensive RVM having the same functionality using energy-efficient IoT MCUs. In this paper, some approaches in computer vision and image processing and their application to the problem of automatic recognition of empty recyclable containers (bottles and cans) and detecting fraud were considered. The list of the available methods and frameworks was shortened because SoC and IoT controllers have memory and computational restrictions. The RVM’s task is the classification of the image inside the RVM by three possible classes: PET bottle, aluminum can or fraud (everything that doesn’t match PET bottle or can), even if cans or bottles are twisted or jammed. Finally, the performance of image recognition procedures in Python and C ++ languages was analyzed and some methods of efficient image processing and RVM structure enhancements to achieve competitive advantages were proposed.
Morphology of Nanowire Arrays Produced by Metal-Assisted Chemical Etching on Si Wafers of Different Types: Comparative Analysis

The results of studies on fabrication of silicon nanowires on the surface of silicon wafers of different types using the metal-assisted chemical etching method are presented. The influence of the deposition time of Ag particles on their morphology and filling density on the surface of silicon wafers has been analyzed. At different etching time of wafer samples of n-type in a HF-H2O2 based etching solution, vertically aligned arrays of solid nanowires are obtained. It is shown that changing the duration of Ag nanoparticles deposition at the same etching time, it is possible to change the distance between the nanowire stacks. It was established that under identical technological conditions the nanowires were not formed on a p-Si wafer. The results are discussed from the point of view of the specific etching mechanism of Si wafers with different type of conductivity and crystallographic orientation.
Resource Allocation for Delay QoS-Driven Multicarrier Relay Networks with Energy Harvesting Nodes

This paper aims to improve the effective capacity (EC) while considering multicarrier relay (MR) network comprised of energy harvesting (EH) base station (BS) and relay node under the quality of service (QoS) constraints. Through considering the energy causality constraint imposed on BS and relay node and the delay QoS constraint, we formulate the optimization problem and the optimized subcarrier and power allocation strategies are obtained by using Lagrange dual decomposition and the Karush-Kuhn-Tucker conditions. Furthermore, a suboptimal algorithm is proposed to reduce the computational complexity. Numerical results demonstrate that the proposed schemes perform better in improving the system EC than the existing resource allocation policies for different QoS exponent sets, especially when the delay requirement is strict.
Preliminary model comparison for Dynamic Thermal Rating estimation

The large-scale diffusion of renewable power generators contributes to trigger a crisis in power system components that started to operate closest to their thermal limits, thus increasing the risk of network congestions. To manage with these contingencies induced by Renewable Energy Sources generators, the Transmission System Operators have to implement proper corrective actions. Transmission System Operators usually adopts well-known physical methods, based on weather forecasts, to estimate the temperature of the lines in order to avoid an expensive installation of monitoring devices on their network. However, this estimation often results in inaccurate forecasts of the conductors’ temperature, due to many complexities and parameters which should be considered in the physical model of the line or to not precise weather forecasts. This paper proposes an innovative method based on Artificial Neural Network to evaluate the conductor’s temperature and consequently the Dynamic Thermal Rating in a given overhead line. The results based on real case studies and measures, clearly show the effectiveness and the potential of the proposed method.
Performance Analysis of SAR Image Speckle Filters and its Recent Challenges

Speckle noise is a critical disturbance in the Synthetic Aperture Radar (SAR) image that alters its quality. Initially, the speckle noise in SAR images should be reduced when recognizing and detecting automatic targets. This paper presents the performance analysis of different speckle filters which are applied in SAR images. The performance of individual filters to achieve speckle suppression and its ability to preserve image edges and details are analyzed. The performance analysis of some of the speckle and morphological filters are explored. Some of these filters result in small losses in the information on SAR images. It has also been found that the performance of these filters in the region of homogeneous area tends to increase with respect to the filter window size. Some of the morphological filters provide good performance in preserving the details of an image while suppressing the speckle noise compared to other adaptive filters. Finally, the recent challenges in speckle noise reduction filters are also explored.
Content-Based Publish/Subscribe Communication Model between IoT Devices in Smart City Environment

In recent years the population of the cities has been increasing the getting over half of the whole world population. These peoples are facing with some security and infrastructural needs. Some of these needs can be met with the use of some smart technologies such as Internet of Things Devices (IoT) with different types of sensors. Coordination and communication of these devices are very critical for enabling the digital solutions in a secure and comfortable city life. However, due to the restricted ability of these devices, setting up a flexible communication platform is a very challenging issue. In this paper for setting up the physical security of smart homes/buildings, a content-based publish-subscribe model is proposed by the use of wireless sensor network nodes in the critical environment. Due to the energy constraint of the devices, the broadcasting feature of the wireless communication is not used, instead of this a peer to a communication according to the content of the message is preferred. Experimental results showed that the proposed communication model can be applicable for smart city environments.
A Machine Learning Approach to Service Rule Conversion

This paper proposed a scheme that automatically converts service rules of cloud manufacturing to semantic rules. The scheme classifies the semantics of typical service rules into several semantic categories. Sentence embedding is used to represent the semantics of service rules. A logistic regression classifier is used to identify the semantic category of a service rule according to its sentence embedding. Based on the identified semantic category, the service rule is converted to a semantic rule. The evaluation of the scheme shows that the scheme achieves good conversion accuracy.
A High Dynamic Range CMOS-MEMS Accelerometer Array with Drift Compensation and Fine-Grain Offset Compensation

This paper presents a high dynamic range CMOS-MEMS capacitive accelerometer. An array of small masses enables the accelerometer to survive and measure high-G (kG) acceleration. A fine-grain offset compensation technique suppresses the offset due to sense capacitance mismatch. The key idea is to employ two out of 184 accelerometer cells to tune out the charge imbalance, which ensures 90X improved tuning resolution (sub-aF offset). Multiple temperature sensors are integrated on the same accelerometer chip to improve the accelerometer long-term bias instability. The prototype is fabricated in standard 0.18 μm CMOS process and then post-processed to release the accelerometer sensing structures. Measurement results demonstrate that the accelerometer achieves 5. 06μV/G transducer scale factor, sub-aF offset, 4.4 μG√hr velocity random walk (VRW), and 5.2 mG bias instability. The sensor array gun-launched at 50 kG and has demonstrated high-shock survivability. Offset measurement results demonstrate 90X better offset tuning resolution than conventional technique.
Quadratic Convex Relaxation based centralized OPF for Multi-Phase Active Distribution Networks

Semi-Definite Programming (SDP) and Second Order Cone Programming (SOCP) relaxations are state-of-the-art lift-and-project based techniques for solving the non-convex AC optimal power flow problem in multi-phase active distribution networks. A novel centralized Quadratic Convex (QC) relaxation for such networks, which is a significant departure from the liftand-project based approaches, is developed in this paper. The proposed scheme encloses the non-convex region, associated with the non-linear terms of power flow equations represented in the polar form, by appropriate convex envelopes. The envelopes for the trigonometric terms are based upon the first order Taylor series approximation and cosecant/secant functions, whereas bilinear terms are enclosed by McCormick envelopes. Furthermore, appropriate bounds on the lifted variables are also introduced and the trilinear function is enclosed by the recursive application of the McCormick envelope. The application of the proposed scheme on several test cases reveals that it outperforms SOCP in all the cases and is close to the SDP relaxation. Furthermore, it has also shown to have high computational efficiency as compared to the SDP approach due to the existence of mature solving technology for quadratically constrained problems.
Causality and Information Transfer Across Time Scales

An information-theoretic approach for detecting interactions and information transfer between the two systems is extended to interactions between dynamical phenomena evolving on different time scales of a complex, multiscale process. The approach is applied to about a century-long records of daily mean surface air temperature from various European locations. An information transfer from larger to smaller time scales has been observed as the influence of the phase of slow oscillatory phenomena with the periods around 6–11 years on the amplitudes of the variability characterized by the smaller temporal scales from a few months to 4–5 years. These directed cross-scale interactions have a non-negligible effect on interannual air temperature variability in a large area of Europe.