Miracle-dz/UniFAR-FTUs huggingface dataset:
Protection and Control of 3 Phase Induction Motors in Industrial Plants with Smart Remote Monitoring and Control on a Centralized System

Induction motors have become an integral part of automation& manufacturing industries as well as used in domestic and agricultural purposes. Induction motors have different specifications for various load requirements. Running these motors in unspecified load conditions will cause damage to the motor or permanently mutilate them. If these undesirable situations are prevented and managed from occurring, motors can be secured and the machinery or automation connected to them will be out of danger. This article describes the working of a three-phase induction motor protection system based on RISC microcontroller PIC18F4520. The use of the microcontroller completely sidelines the previously used conventional protection systems, it also paves the way for fast and simpler computing. The system checks if the phases are in the correct order, checks whether the parameters are within the undercurrent over current, load voltage limits which are set by the user and checks if there are any imbalances in the three-phase motor load. If the microcontroller senses any anomalies in the working of the induction motor, it takes suitable control measures to prevent the motor from getting damaged. A network system is also incorporated to monitor and control the motors remotely over a centralized location. A graphical user interface webpage is used at the monitoring station to monitor, control and change the variable site parameters. This system can be utilized by any motor regardless of its specifications by feeding the right undercurrent and over current limits.
Design and Development of a Salticid Inspired Jumping Robot

approved: Ross Hatton The ability to jump allows small robots to traverse rough terrain, clear large obstacles, and quickly reach elevated locations. These abilities have applications in a variety of areas such as, search and rescue, environmental monitoring, and urban warfare. The primary focus in current state-of-the-art jumping robots has been on jumping as high or far as possible, with little focus on how to change the prelaunch dynamics to accomplish different trajectories. This lack of variety limits the utility of jumping robots by reducing their effective workspace. To solve this problem, we explored the jumping behavior of the Salticid family of jumping spiders. Salticid spiders are able to change their leg geometry and use their front legs to channel jump energy into a vaulting motion. Vaulting allows the spider to achieve flatter trajectories than are possible by extension of the rear legs alone. We mapped the spiders jumping mechanism onto a four-bar linkage to develop a model for the spiders jumping mechanics. This model is then used to develop a robot that is able to effectively mimic the spider’s jumping behavior. c ©Copyright by Ramsey Tachella May 11, 2016 All Rights Reserved Design and Development of a Salticid Inspired Jumping Robot
Underwater Fish Object Detection based on Attention Mechanism improved Ghost-YOLOv5

Object detection is a popular research field in deep learning. People usually design large-scale deep convolutional neural networks to continuously improve the accuracy of object detection. However, in the special application scenario of using a robot for underwater fish detection, due to the computational ability and storage space are limited, which leads to the problem of low recognition accuracy of underwater fish. In this paper, an improved Ghost-YOLOv5 network based on attention mechanism is proposed, and use Ghostconvolution in GhostNet to replace the convolution in YOLOv5. Which reduces the number of parameters of the model and makes the network more lightweight. At the same time, we propose a new attention mechanism added to the feature extraction network to enhance the feature expression of fish objects and the robustness of the model. The experimental results show that compared with the original algorithm, the improved YOLOv5 network reduces the calculation amount of the model, and also has better detection performance, the mAP value increased by about 5%.
Multi-view Image Display System Based On FPGA

With the development of large-scale integrated circuit, computer technology and communication technology, there are more and more applications related to multi eye image display. Image can be used as the carrier of information and is essential in daily life. In order to promote the development of image acquisition and display technology, this paper shows a multi-channel video stream on-screen display system designed on the FPGA platform. The video source is displayed on the same screen, and each video stream can be individually controlled whether it is displayed or not, and the current frame video stream can be stored to the SD card. Finally, through debugging, two different images are successfully displayed on the same display screen through the camera, and the design index requirements of multi-channel image acquisition are met.
Hybrid Model for Customer Segmentation Based on RFM Framework

With the development of big data technologies in recent years, companies that can efficiently use customer data are gaining an edge in the market competition. For e-commerce platforms, by customer segmentation, the cost of marketing can be effectively reduced, customer satisfaction can be improved, and customer lifecycle value (CLV) can be increased. However, in the past RFM models and K-means algorithms, high-dimensional data could not be taken into consideration and the accuracy rate is low. Here we propose a new method that employs entity embedding to handle the category variables and uses the GBDT algorithm for feature extraction and then uses MLP for prediction to classify customers into eight categories. This new method can significantly improve the accuracy rate. Furthermore, this class label is based on the RFM model, which can efficiently identify customers’ value by supervised learning and provide strong support for the customer segmentation strategy of e-commerce platforms. We validate the effectiveness of the proposed approach on a real-world dataset of customer consumption information. The experimental results illustrate that our model significantly outperforms baseline models. The accuracy of proposed in this paper increased by about 20.4% relative to GBDT and about 3.8% relative to MLP.
Semi-Autonomous Teleoperation via Learning Non-Prehensile Manipulation Skills

In this paper, we present a semi-autonomous teleoperation framework for a pick-and-place task using an RGB-D sensor. In particular, we assume that the target object is located in a cluttered environment where both prehensile grasping and non-prehensile manipulation are combined for efficient teleoperation. A trajectory-based reinforcement learning is utilized for learning the non-prehensile manipulation to rearrange the objects for enabling direct grasping. From the depth image of the cluttered environment and the location of the goal object, the learned policy can provide multiple options of non-prehensile manipulation to the human operator. We carefully design a reward function for the rearranging task where the policy is trained in a simulational environment. Then, the trained policy is transferred to a real-world and evaluated in a number of real-world experiments with the varying number of objects where we show that the proposed method outperforms manual keyboard control in terms of the time duration for the grasping.
GuardNN: secure accelerator architecture for privacy-preserving deep learning

This paper proposes GuardNN, a secure DNN accelerator that provides hardware-based protection for user data and model parameters even in an untrusted environment. GuardNN shows that the architecture and protection can be customized for a specific application to provide strong confidentiality and integrity guarantees with negligible overhead. The design of the GuardNN instruction set reduces the TCB to just the accelerator and allows confidentiality protection even when the instructions from a host cannot be trusted. GuardNN minimizes the overhead of memory encryption and integrity verification by customizing the off-chip memory protection for the known memory access patterns of a DNN accelerator. GuardNN is prototyped on an FPGA, demonstrating effective confidentiality protection with ~3% performance overhead for inference.
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation

In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose signiﬁcant challenges for black-box optimisers. Based on these ﬁndings, we propose a Heteroscedastic and Evolutionary Bayesian Optimisation solver ( HEBO ). HEBO performs non-linear input and output warping, admits exact marginal log-likelihood optimisation and is robust to the values of learned parameters. We demonstrate HEBO ’s empirical eﬃcacy on the NeurIPS 2020 Black-Box Optimisation challenge, where HEBO placed ﬁrst. Upon further analysis, we observe that HEBO signiﬁcantly outperforms existing black-box optimisers on 108 machine learning hyperparameter tuning tasks comprising the Bayesmark benchmark. Our ﬁndings indicate that the majority of hyper-parameter tuning tasks exhibit heteroscedasticity and non-stationarity, multi-objective acquisition ensembles with Pareto front solutions improve queried conﬁgurations, and robust acquisition maximisers aﬀord empirical advantages
Multi-view-based automatic method for multiple diseases screening in retinal OCT images

Optical coherence tomography (OCT), a non-invasive high-resolution imaging technology of retinal tissues, has been widely used in the diagnosis of retinal diseases. However, the shortage of ophthalmologists and the overloaded work have caused great difficulties in screening for retinal diseases. Therefore, developing an accurate automatic diagnosis system for screening retinal diseases in OCT images is essential for the prevention and treatment of retinal diseases. To this end, we propose a novel multi-view-based automatic aided diagnosis method for simultaneously screening multiple diseases in retinal OCT images. First, we collected 11,211 cases of 11 common retinal diseases from the ophthalmology clinic, and each case included two OCTs acquired from different views. Then, to automatically and accurately screen diseases in retinal OCT images, a novel multi-view attention network is proposed for screening retinal diseases based on the collected data. Finally, we conduct experiments based on the collected clinical data to evaluate the performance of the proposed method. The AUC of the proposed method achieves 0.9023, which indicates the effectiveness of the proposed method.
Performance measurement in humanitarian aid organization : the case of the Norwegian Red Cross

iii CHAPTER 1: INTRODUCTION. 1 1.1 Motivation of the study . 2 1.2 Problem statement / Research questions . 3 1.3 Introduction to three theoretical approaches . 4 1.4 Limitations on the Scope of the Study. 5 1.5 Disposition of the Thesis . 6 CHAPTER 2: THE FRAME OF REFERENCE.7 2.1 Performance measurement as part of management control . 7 2.2 Choice of three approaches . 8 2.3 A technology approach . 11 2.3.1 Definitions of performance measurement . 11 2.3. 2 Use of key performance indicators . 17 2.3. 3 Performance measurement in profit organizations: A brief overview . 19 2.3. 4 Measuring performance in non-profit organizations . 20 2.3. 5 Challenges in measuring performance in non-profit organizations . 21 2.4 An Accountability Approach . 22 2.4. 1 Accountability overview . 23 2.4. 2 The Purpose of the accountability . 25 2.5 Contingency approach . 28 Contingency Variables . 30 2.6 Summary . 32 CHAPTER 3: METHODOLOGY. 34 3.1 Discussion of Philosophical Position . 34
Modeling and Control of Zero Sequence Current in Paralleled Three-Phase Four-Leg Inverter

Focusing on zero sequence current control in the directly paralleled three-phase four-leg inverter,the model of two directly paralleled inverter is established based on the phase-leg average theory,then the dynamic averaged model of zero sequence is deduced to reveal the generation mechanism of zero sequence circulation.The sinusoidal pulse width modulation(SPWM) in inverter is employed,a new zero sequence current control for directly paralleled three-phase four-leg inverter is then proposed based on the fourth-leg current closed loop,which is able to solve the zero sequence current control and suppress the zero sequence circulating current flowing through the four-legs between two directly paralleled inverters.The simulation and experiment indicate that the proposed control strategy effectively solves the zero sequence circulation in directly paralleled inverter,and provides technical support for high power new energy network expansion by the inverter in microgrid.
Why PPP Modeled Infrastructure Projects Failed:A Critical Review with a Special Focus on Road Sector

— Infrastructure is the back bone of economic development of any Nation. Road infrastructure plays key role for trade and commerce, connecting the production and consumption centers. infrastructure construction is highly capital intensive, wherein the government alone cannot meet its ends and initiated Public Private Partnership (PPP) for its execution right from planning and designing to its maintenance through various PPP models. Over the last few years many of the awarded roadprojects through PPP modelare stalled citing various reasons. This technical paper analyses the risk factors associated with PPP – toll operated road projects through case studies and suggested corrective measures like shadow tolling and hybrid models for restoration of PPP.
Multivariate correlations discovery in static and streaming data

Correlation analysis is an invaluable tool in many domains, for better understanding data and extracting salient insights. Most works to date focus on detecting high pairwise correlations. A generalization of this problem with known applications but no known efficient solutions involves the discovery of strong multivariate correlations, i.e., finding vectors (typically in the order of 3 to 5 vectors) that exhibit a strong dependence when considered altogether. In this work we propose algorithms for detecting multivariate correlations in static and streaming data. Our algorithms, which rely on novel theoretical results, support two different correlation measures, and allow for additional constraints. Our extensive experimental evaluation examines the properties of our solution and demonstrates that our algorithms outperform the state-of-the-art, typically by an order of magnitude.
Random Access Identifier-Linked Receiver Beamforming with Transmitter Filtering in TDD-Based Random Access

SUMMARY This paper proposes a novel random access identiﬁer (RAID)-linked receiver beamforming method for time division duplex (TDD)-based random access. When the number of receiver antennas at the base station is large in a massive multiple-input multiple-output (MIMO) scenario, the channel estimation accuracy per receiver antenna at the base station receiver is degraded due to the limited received signal power per antenna from the user terminal. This results in degradation in the receiver beamforming (BF) or antenna diversity combining and active RAID detection. The purpose of the proposed method is to achieve accurate active RAID detection and channel estimation with a reasonable level of computational complexity at the base station receiver. In the proposed method, a unique receiver BF vector applied at the base station is linked to each of the M RAIDs prepared by the system. The user terminal selects an appropriate pair comprising a receiver BF vector and a RAID in advance based on the channel estimation results in the downlink assuming channel reciprocity in a TDD system. Therefore, per-receiver antenna channel estimation for receiver BF is not necessary in the proposed method. Furthermore, in order to utilize fully the knowledge of the channel at the user transmitter, we propose applying transmitter ﬁltering (TF) to the proposed method for e ﬀ ective channel shortening in order to increase the orthogonal preambles for active RAID detection and channel estimation prepared for each RAID. Computer simulation results show that the proposed method greatly improves the accuracy of active RAID detection and channel estimation. This results in lower error rates than that for the conventional method performing channel estimation at each antenna in a massive MIMO environment.
HEALTH MONITORING SYSTEM AND SMART DOOR FOR MARK OFF BODY TEMPERATURE

: The beginning of Covid-19 has commenced in the city called Wuhan, China. It was a severe respiratory disease. As a consequence of the huge spread and infection, The WHO (World Health Organization) has declared the novel coronavirus (COVID-19) as a universal pandemic. The Flu began spreading its wings around the globe. During this pandemic time, public places are gathering more crowd. There will be a lot of chances to spread the virus. This study exhibits an IOT-based system that is a real-time health monitoring system making use of the uniform values of temperature, and blood pressure of people, which are paramount measurements essentials for critical care. The proposed model can be used for any shopping mall, hotel, apartment entrance. This system has an LCD Display that shows the above constraints.
ES  FR  IT  PL SOCIETAL CHALLENGES - Health, demographic change and well-being SOCIETAL

physical activity and gender, and environmental, socio-economic, occupational and climate-related factors); improving health promotion and disease prevention; understanding disease and improving diagnosis and prognosis; developing eﬀective prevention and screening programmes and improving the assessment of disease susceptibility; improving the surveillance of infectious diseases and preparedness for combating epidemics and emerging diseases; developing new and better preventive and therapeutic vaccines and drugs; using in-silico medicine for improving disease management and prediction; developing regenerative medicine and adapted treatments, and treating disease, including palliative medicine; transferring knowledge to clinical practice and scalable innovation actions; improving health information and better collection and use of health cohort and administrative data; standardised data analysis and techniques; active ageing, and independent and assisted living; individual awareness and empowerment for self-management of health; promotion of integrated care, including psychosocial aspects; improving scientiﬁc tools and methods to support policy making and regulatory needs; optimising the eﬃciency and eﬀectiveness of healthcare provision; and reducing health disparities and inequalities by evidence-based decision making and dissemination of best practice and by innovative technologies and approaches. Active involvement of healthcare providers must be encouraged in order to secure rapid take-up and implementation of results.
The application of compaction soil-cement pile

The compaction soil-cement pile is an economic and practicable foundation treatment plan with low cost, high bearing capacity and environment protection function. In this paper based upon its application in the engineering of Yangquan Hetan national grain depot construction checking show that all piles meet the design requirements. This kind of foundation treatment measures is worthy of being applied in loess area and back filled region.
DELVE IN THE NEW ERA: ARTIFICIAL INTELLIGENCE IN STRUCTURAL ENGINEERING

: Artificial Intelligence (AI) is that branch of computer science that develops machines and software with humanlike intelligence. Artificial Intelligence proves to be the most promising path to more efficient practices in civil engineering. AI can be successfully implemented as a vital game changer in the field of Structural engineering to determine engineering design parameters when testing is not possible. Even with the vast range of adaptability that AI possesses, it is never, at least in the near future that AI is to be considered to perpetually replace human involvement as it can never account for the rationality that is purely human in possession. On the contrary, the essence of it is to be touchwood to assist and help in the field of structural engineering to expand their workflow. The complex and deep-learning algorithms possessed by modern AI systems provide clear cut platforms to the engineers and is something to be put resources into.
Calculation of Complex Power Generated by a Transmitting Thin Wire Antenna Radiating above a Lossy Half-space

— The paper deals with an efficient approach to determine complex power generated by a thin wire antenna above a lossy ground. Once the current distribution along a thin wire is obtained by numerically solving the Pocklington integro-differential equation the complex power of the antenna can be obtained by solving the integral over the inner product of tangential component of the electric field and current distribution along the wire stemming from Poynting theorem. Numerical calculation procedure uses the vectors and matrices already constructed within the calculation procedure for the current distribution. Some illustrative numerical results for the active power, reactive power and apparent power of the dipole antenna radiating over a lossy half-space are presented in the paper for different values of input parameters.
Atomistic-Level Description of the Covalent Inhibition of SARS-CoV-2 Papain-like Protease

Inhibition of the papain-like protease (PLpro) of SARS-CoV-2 has been demonstrated to be a successful target to prevent the spreading of the coronavirus in the infected body. In this regard, covalent inhibitors, such as the recently proposed VIR251 ligand, can irreversibly inactivate PLpro by forming a covalent bond with a specific residue of the catalytic site (Cys111), through a Michael addition reaction. An inhibition mechanism can therefore be proposed, including four steps: (i) ligand entry into the protease pocket; (ii) Cys111 deprotonation of the thiol group by a Brønsted–Lowry base; (iii) Cys111-S− addition to the ligand; and (iv) proton transfer from the protonated base to the covalently bound ligand. Evaluating the energetics and PLpro conformational changes at each of these steps could aid the design of more efficient and selective covalent inhibitors. For this aim, we have studied by means of MD simulations and QM/MM calculations the whole mechanism. Regarding the first step, we show that the inhibitor entry in the PLpro pocket is thermodynamically favorable only when considering the neutral Cys111, that is, prior to the Cys111 deprotonation. For the second step, MD simulations revealed that His272 would deprotonate Cys111 after overcoming an energy barrier of ca. 32 kcal/mol (at the QM/MM level), but implying a decrease of the inhibitor stability inside the protease pocket. This information points to a reversible Cys111 deprotonation, whose equilibrium is largely shifted toward the neutral Cys111 form. Although thermodynamically disfavored, if Cys111 is deprotonated in close proximity to the vinylic carbon of the ligand, then covalent binding takes place in an irreversible way (third step) to form the enolate intermediate. Finally, due to Cys111-S− negative charge redistribution over the bound ligand, proton transfer from the initially protonated His272 is favored, finally leading to an irreversibly modified Cys111 and a restored His272. These results elucidate the selectivity of Cys111 to enable formation of a covalent bond, even if a weak proton acceptor is available, as His272.