Miracle-dz/UniFAR-FTUs huggingface dataset:
Micro AUV Localization for Agile Navigation with Low-cost Acoustic Modems

High-rate self-localization for agile navigation still constitutes a major bottleneck in micro autonomous underwater vehicle (µAUV) research. Hereby, key challenges arise from the system being small-size and low-cost, which excludes common approaches such as relying on sophisticated ring-laser gyroscopes. In this context, we investigate two promising sensor fusion strategies for µAUVs based on low-cost sensors and motion models. We benchmark our findings in experiments against a baseline linear least-square estimator and a GPS reference track.
Fabrication of MoS2/ZnO Hybrid Nanostructures for Enhancing Photodetection

In this article, the effect of MoS<sub>2</sub> and ZnO blend ratio on the performance of MoS<sub>2</sub>:ZnO hybrid photodiodes is investigated. The UV and visible performance parameters for five different devices consisting of semiconducting layers like MoS<sub>2</sub>, ZnO, MoS<sub>2</sub>:ZnO (1:1), MoS<sub>2</sub>:ZnO (1:2), and MoS<sub>2</sub>:ZnO (2:1) each are analyzed. The device MoS<sub>2</sub>:ZnO (1:2) shows the best performance in the UV region with a responsivity of 17.04 A/W, detectivity of <inline-formula> <tex-math notation="LaTeX">$6.84\times 10 ^{\mathbf {13}}$ </tex-math></inline-formula> Jones and external quantum efficiency (EQE) of 6604% at 320nm, −1 V bias. On the other hand, the device MoS<sub>2</sub>:ZnO (2:1) gives the best performance in the visible region with a responsivity of 2.27 A/W, detectivity of <inline-formula> <tex-math notation="LaTeX">$0.60\times 10 ^{\mathbf {13}}$ </tex-math></inline-formula> Jones, and EQE of 533% at 529nm, −1 V.
Managing Information Flows from Companies to Customers : Impact of commercial world-of-mouth in social media

In the article we compare the communication of the companies towards customers on social networks. We measure the effectiveness of communication by comparing groups managed by sponsored brand ambassadors and managed by regular non-affiliated users. The article measures the reactions to this corporate communication (likes) and how users interact to the corporate communication (comments). This is compared to how users respond and comment in non-corporate groups. We obtain data using our own parser after downloading it from a specialized social network, the blue horse, focused on parents and children. Although the research is done on a smaller and specialized network, it is safe to expect, that similar principles apply in targeted groups on larger networks such as Facebook as well. Both formal and informal promotion from a paid ambassador increases the average user activity in the group, rising not just the number of contributions, but also engagement (ie comments and likes) and brand awareness. Such comparison of activity and communication in corporate and unmanaged groups is unique.
Dynamic Performance Improvement of Piezoelectrically Driven Micro-Lens Actuators

This letter presents experimental results showing significant enhancement in the dynamic performance of a novel piezoelectric micro-lens actuator using a robust feedback resonant controller. The design of the feedback controller is based on experimentally identified model of the micro-lens actuator. The stability of the closed-loop system is proven by the negative imaginary theory. The experimental results show that the resonance peak and the settling time of the actuator are reduced by 27 dB and to 2.5 ms from 119 ms, respectively. Such improvement demonstrates the capability of the piezoelectric micro-lens actuator for fast auto-focus application in miniaturized camera. The reported performance makes it the fastest micro-lens actuator compared to other previously reported micro-lens actuators. [2020-0313]
SOT-MRAM based Analog in-Memory Computing for DNN inference

Deep neural network (DNN) inference requires a massive amount of matrix-vector multiplications which can be computed efficiently on memory arrays in an analog fashion. This approach requires highly resistive memory devices $(> \mathrm{M}\Omega)$ with low resistance variability to implement DNN weight memories. We propose an optimized Spin-Orbit Torque MRAM (SOT-MRAM) as weight memory in Analog in-Memory Computing (AiMC) systems for DNN inference. In SOT-MRAM the write and read path are decoupled. This allows changing the MTJ resistance to the high levels required for AiMC by tuning the tunnel barrier thickness without affecting the writing. The target resistance level and variation are derived from an algorithm driven design-technology-co-optimization (DTCO) study. Resistance levels are obtained from IR-drop simulations of a convolutional neural network (CNN). Variation limits are obtained by testing two noise-resilient CNNs with conductance variability. Finally, we demonstrate experimentally that the requirements for analog DNN inference are met by SOT-MRAM stack optimization.
Model-Free Control of Time-Delay Systems via Policy Gradient Based Adaptive Learning Algorithm

This paper develops a model-free optimal control scheme for discrete-time nonlinear systems with time-delays by using the policy gradient based adaptive learning (PGAL) algorithm. By using the measured data, the PGAL algorithm is employed to design an optimal controller for discrete-time systems. Compared with the traditional adaptive dynamic programming algorithms, the proposed method is a data-based one and improves the control input with policy gradient. The convergence of the PGAL algorithm is proved by demonstrating that the value function converges to optimum. To implement the PGAL algorithm, an actor-critic framework is constructed to learn the optimal control law and the value function. Finally, a simulation example is presented to demonstrate the effectiveness of the developed method.
Stability Analysis and Adaptive Resonance Damping of Multi-Converter System Applying Bidirectional Converter

Bidirectional converters have become extensively applied in various dc power systems that contain battery energy storage systems (BESSs). Such dc power systems typically consist of several feedback-controlled converters, forming a complex power distribution system. Consequently, a number of stability issues arise due to interactions among multiple converter sub-systems. Recent studies have presented adaptive control methods for guaranteeing the stability of a multi-converter system under varying operating conditions. However, most methods have not fully considered the bidirectional power-flow in BESS that significantly affects the system dynamics and stability analysis. This paper demonstrates how a bidirectional converter can be operated as an adaptive virtual impedance to improve overall system performance. In the method, an adaptive resonance term is added to the converter’s voltage controller to damp resonances in the network’s bus impedance, thus improving voltage damping and guaranteeing stability. The reliability and performance of the multi-converter system can be maintained under varying operating conditions by updating the bidirectional converter controller structure so that the required bus voltage damping level is guaranteed.
Characteristics of Power Module with New Advanced Substrate

In the paper, we introduce thermal, electrical and long term reliability characteristic of insulated metal substrate (IMS) and comparing to the conventional DBC+Baseplate structure. Thermal performance is determined by junction to case thermal resistance, and the result shows that the thermal resistance of aluminum base IMS is reduced about 2.7% compared to conventional structure. Moreover, copper base IMS can reduce another 18% of thermal resistance. In Q3D simulation, the stray inductance of IMS is smaller than conventional structure about 43%. From the reliability point of view, thermal cycle test is also performed for 200 cycles which temperature range from $-65 ^{\circ}{C}$ to $150 ^{\circ}{C}$ with dwell time 30 minutes. DBC+Baseplate structure generate severely delamination under CSAM inspection in first 50 cycles. In contract to DBC+Baseplate, IMS shows no defect to the end of the experiment.
Three-dimensional Reconstruction of Single Pipeline Radiographic Image

In view of the difficulties and low precision in the process of three-dimensional reconstruction of single pipeline radiographic image, this paper proposes a three-dimensional reconstruction method by establishing a model of the relationship between object thickness and image gray scale. The method firstly uses image preprocessing segmentation algorithm to segment the welding seam area, calculates the thickness of the object in the known area and reads its corresponding image gray level with iterative algorithm, then constructs an object thickness- gray level model, uses BP neural network to solve the model parameters, uses the model to calculate the area to be solved and generate point cloud data, and finally processes the point cloud to complete three-dimensional reconstruction. Experiments show that the method has simple reconstruction steps and high efficiency, can effectively carry out three-dimensional reconstruction on a single ray image of a pipeline, and the reduction rate of the maximum welding beading height can reach 91.7%, thus providing a new idea for the three-dimensional reconstruction of a single ray image taken in industry.
Light Control Smart Farm Monitoring System with Reflector Control

This paper is on the implementation of a system that monitors and controls a smart farm using Arduino and DC motors. Light control in traditional smart farms uses artificial light such as LED lights to control brightness. However, this traditional method has high maintenance cost for continuously turning on artificial light. In this paper, we develop a system to control the amount of light inflow using the angle control of the reflector. Also, we monitor environmental information such as temperature, humidity, carbon dioxide(Co2), and light value for the optimum smart farm environment. The temperature is controlled using the ventilator and heater. Also, environmental data can be uploaded to the server in real-time to check the accumulated data on a chart, and we accumulate the optimal reflector angle data for more than one year. Since solar motion repeats every year, we can control the reflector according to this accumulated data. This system has been implemented as a server and mobile application that provides various sensors for environmental control, Arduino, Wemos for Wifi server upload, and a monitoring UI.
Metrology Model of Measuring Channel in Multi-Channel Data-Measurement System

Developing metrology solutions is of major importance when building data-measurement systems. One of the key issues is evaluating the characteristics of measuring channels built on the basis of digital sensors of physical values. These sensors include a primary analog transducer and an analog-digital converter. The study evaluates the occurrence of errors in measuring channels of a multi-channel data-measurement system. This study deals with the measuring channel built on the basis of digital temperature sensor employing contact measurement. The digital temperature sensor is an integral body that combines both the primary temperature transducer and the primary data processing units. Drawing on the analysis of various errors the study develops the abstract metrology model of the measuring channel. This model takes into account the errors related to inherent instrument errors as well as the errors induced by the structure of the measuring subsystem. The work presents calculations of multiplicative and additive errors of the measuring channel. It also suggests measures for minimizing the additive errors of the measuring channel in the system.
Comparison of Hybrid Intrusion Detection System

IDS have an important role in dispelling and preventing an intrusion or abuse of access rights. In its development, research on IDS is growing. IDS consist of several detection models, one of which is hybrid-based, which in IDS detection combines the signature and anomaly models. This method is considered more effective because it combines the advantages of the speed of signature detection and the ability to analyze new attacks from the anomaly model. However, from existing models and frameworks hybrid-based IDS still needs to be further developed to be implemented in the industry. From a number of existing IDS-based studies, this paper intends to conduct a review with the aim that researchers who wish to develop hybrid-based IDS know which methods and architecture are best to be implemented. This paper reviews hybrid IDS research in the last five years.
MLB-PoL: A High Performance Hybrid Converter for Direct 48 V to Point-of-Load Applications

There is an increasing need for more efficient power conversion from 48 Vdc to point-of-load (PoL) applications in datacenters. This work presents a new hybrid converter with Multi-Level Binary (MLB) voltages on the flying capacitors that is well-suited for PoL applications with very high conversion ratios. The proposed MLB-PoL converter can be viewed as an 8-to-1 multi-phase doubler switched-capacitor (SC) converter merged with a two-phase interleaved buck converter. Compared to other two-phase hybrid SC topologies, multi-phase operation can help achieve higher conversion ratio in the SC stage with an equal or fewer number of components, and thus reduce the switch and inductor stress of the following buck stage. In addition, the output inductors in the proposed topology benefit from a frequency multiplication effect similar to that of the flying capacitor multilevel (FCML) converter. This can help further reduce the inductor size without increasing the switching frequency. A 48 V to 2.5-1.0 V converter prototype with 65 A output current was built and tested. At 48 V to 2 V, the prototype achieved 95.1% peak efficiency (94.3% including gate drive loss) and 395 W/in3 power density.
A Novel V-shape Interior Permanent Magnet Synchronous Machine with Asymmetric Spoke-type Flux Barrier

This paper proposes an asymmetric interior permanent magnet (AIPM) synchronous machine with symmetrical V-shape PMs and asymmetric spoke-type flux barriers. This asymmetric rotor structure is featured by unequal distances between the flux barrier and its adjacent PMs, which can utilize the magnetic-field-shifting (MFS) effect by reducing the current angle difference between the maximum PM and reluctance torque components. Consequently, compared with the conventional V-shape IPM design with the same PMs, the novel topology can achieve resultant torque enhancement while no extra cost is required. Influences of position and dimensions of asymmetric flux barriers and pole arc of V-shape cavity on the maximum average torque and open-circuit air-gap flux densities are investigated. The proposed AIPM and the conventional V-shape IPM benchmark are designed by global parametric optimization with the same stator, rotor diameters and PMs. The comparison between electromagnetic performances confirms that the torque enhancement of the proposed AIPM is achieved by the MFS effect.
Feature Engineering for Grid-based Multi-Floor Indoor Localisation using Machine Learning

Nowadays, the need for indoor localization is increasing as it has many possible implementations in many sectors, e.g. navigation, health care, etc. In order to obtain an accurate indoor location, fingerprinting is the most commonly used method. The problem with fingerprinting approach is the high variation of RSSI values, resulting in erroneous location estimation. Machine learning approach is a new alternative to fingerprinting approach that aims to solve this problem. In this study, we adopt grid-based approach to train the Random Forest models with an end-to-end pipleline to autotune the models hyperparameter. In addition, we explore the impact of features by studying the default RSSI values for undetected AP as well as the feature importance. It highlights the significance of selecting the data pre-processing and the AP's RSSI combinations. RSSI data are collected from WiFi access points (APs) as WiFi access points are widely available and are at fixed location, which does not requires specific hardware support. The evaluation are benchmarked against the self-collected data in the university, called N4, and the publicly available dataset UJIndoorLoc (UJI). The results show that random forest classifier is the overall best performance for classification-based indoor localization, with up to 88.21% and 86.34% for N4 and UJI dataset respectively. By eliminating unimportant APs, the accuracy is maintained at 80% accuracy with 20% of features using PCA. It has shown that our proposed pipeline/framework provides a resilient constructed model in localizing the grid-base location when some of the APs are not detected.
Unsupervised Detection for Burned Area with Fuzzy C-Means and D-S Evidence Theory

Mapping the burned area of forest fires can contribute significantly to the understanding, quantification, and evaluation of forest fire severity and its impacts on the forest ecosystem. In this paper, an unsupervised detection for burned region based on the Fuzzy C-Means (FCM) and Dempster-Shafer (D-S) evidence theory with the bi-temporal images is proposed. Specifically, according to difference maps from the delta normalized burn ratio and spectral angle index, the Expectation-Maximization (EM) algorithm is used to separate the study area into the definitely burned region and indefinitely burned region. Then, under the enlightenment of the multi-source information fusion theory, the indefinite region is discriminated against further with FCM and D-S evidence theory. Finally, the final fire-burned map can be inferred from the results obtained from the aforementioned steps. The experimental results on two forest fires with bi-temporal Landsat-8 images have shown the potential of the proposed burned area mapping method, in the field of detecting the forest landscape change based on multispectral remote sensing images.
An Improved Transfer Learning Method for Bearing Diagnosis under Variable Working Conditions Based on Dilated Convolution

Although intelligent fault diagnosis methods of rolling bearings have been extensively developed, the diagnosis results are usually disturbed by the change of working conditions and environmental noise, which will lead to significant decrease in diagnostic accuracy. Most methods assume that training data and testing data are under the same distribution but it is always impractical in real-world production. To improve diagnostic performance, a new transfer diagnosis method is proposed. On the basis of simultaneously aligning the marginal distribution and conditional distribution of datasets, the entropy penalty is added to the objective function to improve the separability of inter-class features. The dilated convolutional layer with a jagged expansion rate is added to the diagnostic model, and it will better extract the local and global features of the raw signal. The experimental results show that the proposed method can achieve a higher diagnostic accuracy under variable working conditions comparing with related methods and can acquire good robustness under the interference of different intensity gaussian noise.
Design and Construction of a 6 DOF Robot with Ability to move the TCP in Continuous Trajectories

The generation of linear and circular trajectories continuous in time in a physical space is one of the most interesting challenges to obtain in the construction of a 6 DOF industrial robot prototype. Since it involves having a kinematic, dynamic control, trajectory generator and all integrated under efficient operating system. In this work, a 6 DOF robot is designed and implemented with the capacity to generate continuous linear and circular trajectories by means of kinematic control (direct and inverse kinematics, trapezoidal interpolators, trajectory planning) and an inverse dynamic model based on the Newton-Euler formulation for the dimensioning and selection of actuators. Finally, trajectories (point to point, linear and circular) generated in the robot under maximum load conditions are executed in order to check the dynamic performance of the robot.
Design Optimization of a Synchronous Reluctance Motor Based on Operating Cycle

Synchronous reluctance motors are becoming an interesting solution for drives applications requiring high efficiency. The design of those machines is normally based on the optimization of the motor performance at the rated working point in terms of torque production, torque ripple and efficiency, but in many applications the drive will operate for most of the time at partial load and frequent overload may be required, with a speed not necessarily equal to the rated value. In this work we propose an optimized machine design method to maximize the drive efficiency on the total operating cycle, considering a specific speed and torque profile. Four typical working points are selected, each of them maintained by the machine for a different time in the operating cycle. The obtained machine is compared with the one optimized based on the rated conditions only, showing slightly higher losses on a single working point but an improved efficiency on the global operating cycle.
Integrated Path Planning and Tracking Control of Autonomous Vehicle for Collision Avoidance based on Model Predictive Control and Potential Field

This paper attempts to implement an integrated path planning and tracking controller that generates optimal control inputs guaranteeing a collision-free path. This integrated scheme is achieved by unifying model predictive control (MPC) with a potential field for assigning collision risk. The target vehicle is an electrified autonomous vehicle that is capable of directly controlling traction and braking torques of vehicle. Wheel torque and steering input of vehicle are optimized by receding horizon optimization (RHO) and give us stable and comfort reference trajectories. In the optimization process, control inputs, tracking errors, and collision risk are to be minimized in a single objective function. Collision risk is taken into account by modeling proper potential fields that allow the controller to re-plan the given desired path for avoiding a collision. Simulation is conducted using a high-fidelity vehicle plant model and the control scheme demonstrates promising results by verifying optimal and stable path guaranteeing collision-free.
Harvest Crate Detection for Grapes Harvesting Robot Based on YOLOv3 Model

Fast and accurate algorithms for object detection allow robotic systems to real-time process scene information identically to the human visual system. The robotic system of our interest is a grape harvesting robot. The robot will cut the grape bunches suitable for harvesting and collect them in harvest crates located in the vineyard corridors along its path. In this work, a state-of-the-art algorithm, the You-Only-LookOnce version 3 (YOLOv3), is employed to solve the in-field demanding object detection task, that of harvest crates. Complicated environment in-field conditions, such as illumination variations, branches and leaves occlusion, weed and object overlapping, make harvest crates detection challenging. The proposed algorithm is suitable to deal with these problems. Experimental results verify the effectiveness of the model under varying conditions, providing high recognition accuracy up to 99.74 % (mAP). Thus, the model is able to provide reliable information to the harvesting robot, which is the required preliminary step so that the robot could navigate to the harvest crate and place the grape bunches.