Miracle-dz/UniFAR-FTUs huggingface dataset:
Turbine generator laboratory model tests to damp torsional oscillations with supplementary signals

The improvement of generator stability by the use of supplementary signals into the voltage regulator and governor loops using discrete-time linear optimal control theory has been studied with particular emphasis on providing better damping for torsional oscillations. A multi-inertia laboratory model equipped with data acquisition and control computers was constructed to model the shaft dynamics of a 660 MW Drax turbine-generator. It is shown that the shaft torsional phenomena can be adequately simulated on a micro-synchronous-generator at least as far as the dominant shaft torsional modes of vibration are concerned. The practical implementation of multi-mode linear quadratic Gaussian (LQG) controllers has been shown to enhance system stability and provide better damping to the lower frequency torsional modes, which are those most susceptible to excitation. >
Use of a laser stripe illuminator for enhanced underwater viewing

A laser-based illumination system that can be incorporated into the existing underwater viewing system of current remotely operated vehicles (ROVs) has been investigated. A stripe of projected laser light is scanned over the target and resultant image frames are processed and combined to produce a computer generated composite image. Initially, the system was characterized through trials in the underwater optical test facility at Cranfield where water conditions are easily controlled. Subsequently, further work was carried out on a self- contained underwater unit at a flooded quarry in Leicestershire. Several advantages of this technique have been identified from the laboratory and field trials. Scanning the laser source allows greater control of the lighting envelope resulting in more optically uniform images. Contrast improvements of 300% and range improvements of 50% over orthodox systems have been measured. The use of image processing allows a greater degree of flexibility in image presentation and because this methodology is based on a standard underwater camera, it is complementary and can be fitted retrospectively at relatively low cost.
BI-RME modeling of 3D waveguide components enhanced by the Ewald technique

This paper presents the application of the "Boundary Integral-Resonant Mode Expansion" (BI-RME) method in conjunction with the Ewald technique to the modeling of arbitrarily shaped three-dimensional waveguide components. The Ewald transformation enhances the convergence of the Green's functions for a boxed resonator thus making practical the implementation of the BI-RME method in rectangular geometry. The new algorithm becomes particularly efficient if the component shares a significant portion of its boundary with the box. The analysis of a waveguide filter demonstrates the advantage of this approach.
Introduction of a dc bias into a high-Q superconducting microwave cavity

We report a technique for applying a dc voltage or current bias to the center conductor of a high-quality factor superconducting microwave cavity without significantly disturbing selected cavity modes. This is accomplished by incorporating dc bias lines into the cavity at specific locations. The measured S-matrix parameters of the system are in good agreement with theoretical predictions and simulations. We find that at 4 K the quality factor of the cavity degrades by less than 1% under the application of a dc bias.
Performance analysis of an IPM motor with segmented rotor for flux-weakening applications

In this paper, the new rotor structure of an interior permanent magnet (IPM) motor is accurately examined, highlighting how flux-barriers have to be designed to improve the motor performance. To this aim, the magnetic field in the rotor has been studied by a finite element analysis, that has been found to be particularly accurate for this type of motor. The parameters (PM flux linkage and d- and q-axis inductances) and the performance (torque-speed characteristic) of a classical IPM motor are compared with the same items of the novel design.
Genetic Algorithm Based Transmission Expansion Planning System

This paper presents an application of genetic algorithm (GA) to the solution of Static Transmission Expansion Planning (STEP) to determine the optimal number of transmission circuits required in each network corridor and their respective network adequacy while satisfying various economic and technical constraints. The uncertainties in generation and distribution networks as a result of power system deregulation cannot be modeled effectively using the conventional mathematical methods. GA, been a probabilistic based approach has the ability to resolve the transmission expansion planning problem in the face of such uncertainties. The model presented in this work will help in the identification of overloaded lines using Gauss Seidel (GS) load flow technique. Result from GS is then used as input in the GA simulation to show the extra lines needed to accommodate present load flow in the system. The model was tested on IEEE 14 - bus test network.The model developed and simulation results obtained may be useful in an electric power systems undergoing deregulation. Such is the case in Nigeria presently where the generation and distribution components are increasing significantly without any commensurate boost in the transmission sector. This leads to line overloads necessitating commensurate and immediate expansion of the grid.
Effectivity reduction of PWM-converter based dynamic filter by signal processing delay

The nonnegligible time it takes to sample voltages and currents, and to perform some signal processing on the digitized signals to calculate the reference current for a PWM-converter based dynamic filter, results in a time lag of this reference. The consequence of this normally neglected effect is a surprising distortion of the ideal compensated line current. This paper investigates and evaluates this error, proposes a model for studying and analyzing this effect, and presents experimental results to substantiate the analysis.
Fu, Teng; Zhou, Yusheng Stabilization of switched time-delay systems with only unstable subsystems: a new approach based on a vibration model of 1.5 degrees

This paper presents a new state-dependent switching strategy for stabilization of switched time-delay systems with all subsystems being unstable. When time-delays are not small enough, the delayed subsystem can be approximated as a third-order linear delay-free system by using third-order Taylor expansion. Then, a special vibration model with a nonholonomic constraint is introduced to match the obtained third-order linear system. On this basis, the energy function of the original delayed subsystem is constructed by the sum of the kinetic and potential energies of the special vibration model. After that, a state-dependent switching rule with large energy loss in a switching loop is designed by using the energy functions of two delayed subsystems. Finally, excellent agreement is found between our analytical results and the corresponding numerical simulations.
Implantable low power integrated pressure sensor system for minimal invasive telemetric patient monitoring

A new low power integrated pressure sensor system with digital output (1 bit PDM signal) for medical applications is presented. The absolute pressure sensor comprising 400 nm thick surface micromachined polysilicon membranes for capacitive pressure detection and a monolithic integrated 2/sup nd/ order sigma-delta-modulator including voltage reference and timing generator is extremely miniaturized on an area of approximately 3 mm/sup 2/. For protection and biocompatibility reasons the sensor is coated with a silicone elastomer of up to 100 /spl mu/m thickness, which does not influence the sensor's performance. The sensor system was tested in vitro in physiological NaCl solution, showing excellent results compared to a commercially available reference sensor. The sensor system is working well down to a supply voltage of 2.2 V at a power consumption of 0.5 mW. The resolution is better than 12 bit. Due to the small chip area, low power consumption and cost effective production process, the sensor is ideal for medical applications, e.g. in combination with telemetric power and data transmission as an implantable sensor to reduce the mortality risk of intensive care patients.
A CLOSED LOOP MOBILE POWER CONTROL SIMULATOR

In this research project, predictive power control in Code Division Multiple Access (CDMA) systems in urban environments is investigated. Predictive filtering of estimated received signal power has been proposed, and a class of FIR-type polynomial filters has been investigated with simulations. The simulations show that FIR polynomial predictors can provide smoothed signal power samples with the signal-to-noise ratio (SNR) improved by ca. 5 dB, without any delaying of the signal. Driven by these re- sults, a CDMA power control simulator is being constructed for actual controller development in COSSAP (Communications Simulation and System Analysis Program by CADIS GmbH, Germany) envi- ronment. This paper briefly introduces the simulator concepts. As the CDMA systems are inherently interference limited, it is of paramount importance to keep the transmission power of each mobile user as low as possible (1). This is crucial in the uplink transmission (from mobile to base station), where all the mobile units need to be con- trolled by the base station to keep the received power level from each mobile unit constant in the average. The need for power control has been widely studied, and the capacity of a CDMA system is found to greatly depend on the power control function (1), (2). The mobile trans- mitter power control for counteracting Rayleigh fading is achieved through a closed power control loop for which it is necessary to estimate the received power level. A CDMA closed loop power control simulator (3) is depicted in Fig. 1. The simulator consists of mobile transmitter, radio channel, base station receiver, and power controller models. The simulator operates on chip level, and all the models are presented in the complex baseband domain. In the earlier studies, signal-to-noise ratio (SNR) was used as the measure of the pre- dictor performance, and the applicability conditions for a class of FIR polynomial predictors for Rayleigh fading prediction were found (4), (5), (6), (7). While SNR certainly gives impli- cations of the applicability of the predictors, current controller research directly addresses the two main objectives: reducing the variance of the received power level and, reducing trans- mitter power consumption. Both affect the interuser interference, and thus the CDMA system capacity. The latter one naturally also contributes directly to the mobile power consumption. The main question is that how to decide when to apply which transmission power level given limited controller dynamics, while maintaining the same (or better) bit error rate (BER) as with a conventional reference power controller. The new simulator directly outputs actual achieved BERs and variances of the controlled channel output power. Also, relative transmit- ter power consumption is measured by cumulative mobile transmitter power level setting.
Visual Graph Memory with Unsupervised Representation for Visual Navigation

We present a novel graph-structured memory for visual navigation, called visual graph memory (VGM), which consists of unsupervised image representations obtained from navigation history. The proposed VGM is constructed incrementally based on the similarities among the unsupervised representations of observed images, and these representations are learned from an unlabeled image dataset. We also propose a navigation framework that can utilize the proposed VGM to tackle visual navigation problems. By incorporating a graph convolutional network and the attention mechanism, the proposed agent refers to the VGM to navigate the environment while simultaneously building the VGM. Using the VGM, the agent can embed its navigation history and other useful task-related information. We validate our approach on the visual navigation tasks using the Habitat simulator with the Gibson dataset, which provides a photo-realistic simulation environment. The extensive experimental results show that the proposed navigation agent with VGM surpasses the state-of-the-art approaches on image-goal navigation tasks. Project Page: https://sites.google. com/view/iccv2021vgm
Development and application of a vision-based displacement measurement system for structural health monitoring of civil structures

For structural health monitoring (SHM) of civil infrastructures, displacement is a good descriptor of the structural behavior under all the potential disturbances. However, it is not easy to measure displacement of civil infrastructures, since the conventional sensors need a reference point, and inaccessibility to the reference point is sometimes caused by the geographic conditions, such as a highway or river under a bridge, which makes installation of measuring devices time-consuming and costly, if not impossible. To resolve this issue, a visionbased real-time displacement measurement system using digital image processing techniques is developed. The effectiveness of the proposed system was verified by comparing the load carrying capacities of a steel-plate girder bridge obtained from the conventional sensor and the present system. Further, to simultaneously measure multiple points, a synchronized vision-based system is developed using master/slave system with wireless data communication. For the purpose of verification, the measured displacement by a synchronized vision-based system was compared with the data measured by conventional contact-type sensors, linear variable differential transformers (LVDT) from a laboratory test.
THE EFFECT OF SOCIAL MEDIA MARKETING ACTIVITIES ON CUSTOMER-BRAND RELATIONSHIP IN THE EGYPTIAN COSMETIC SECTOR

While social media (SM) is used throughout the world via website-based network apps that assist in exchanging knowledge, information, and simple interactions with others, the actual number of users is believed to be in the millions. Using SM to satisfy several interests is on the rise: people seek digital information and goods. The purpose of this research was to examine the Social Media Marketing Activities (SMMA) that are used to promote trendy goods in the Egyptian cosmetic sector and to determine the extent to which companies' SMMA activities improve their relationships with consumers and drive purchase intention. For this reason, the focus of this research is to examine the impact SMMA has on the customer-brand relationship in the Egyptian cosmetics industry. Additionally, to better address the question, THE EFFECT OF SOCIAL MEDIA MARKETING ACTIVITIES ON CUSTOMER-BRAND . Dr. Amira Mohamed Ahmed Omar دلجملا يناثلا رشع ددعلا ثلاثلا 0202 672 how effective are SMMA initiatives in creating brand-consumer connections and driving purchase intent? The researcher analyzed data from Egyptian cosmetic market respondents to assess the study factors, including social media marketing activities, customer-brand connections, and purchase intention. The researcher used both methods descriptive and fielded analytical approaches and utilized a questionnaire method to gather data. As a result, it is revealed that the relationship between the "customer" and the brand has a significant and positive effect on customers' purchase intention via social media.
Analysis of GTO-based static VAr compensators

The growing availability of high-power gate turn-off thyristors (GTOs) has meant that power electronic convertor circuits can be manufactured with the ability to both absorb and generate reactive power. These circuits constitute the next generation of static VAr compensators (SVCs) which may eventually supersede the conventional thyristor-controlled reactors (TCRs) and thyristor-switched capacitors (TSCs) used in power systems at present. The paper gives an analysis of both 6-pulse and 12-pulse GTO-SVCs based on voltage-source convertor circuits, and equations are derived that describe the performance of each circuit. In addition, the conditions leading to circuit resonances are discussed together with possible methods of avoiding the problems.
The application of an enhanced Brute Force algorithm to minimise energy costs and train delays for differing railway train control systems

This paper demonstrates an enhanced Brute Force algorithm application for optimising the driving speed curve by trading off reductions in energy usage against increases in delay penalty. A simulator is used to compare the train operation performance with different train control system configurations when implemented on a section of high-speed line operating with two trains, including differences in journey time and train energy consumption. Results are presented using six different train control system configurations combined with three different operating priorities. Analysis of the results shows that the operation performance can be improved by eliminating the interactions between trains using advanced control systems or optimal operating priorities. The algorithm is shown to achieve the objectives efficiently and accurately. Control system configurations with intermediate levels of complexity (e.g. European Train Control System Levels 2 and 1 with in-fill) when coupled with the optimisation process have been shown to have similar performance to the more advanced control system.
Real-time Multiple-Workow Scheduling in Cloud Environment

With the development of cloud computing, an increasing number of applications in different fields have been deployed to the cloud. In this process, the real-time scheduling of multiple workflows composed of tasks from these different applications must consider various influencing factors which strongly affect scheduling performance. This paper proposes a real-time multiple-workflow scheduling (RMWS) scheme to schedule workflows dynamically with minimum cost under different deadline constraints. Due to the uncertainty of workflow arrival time and specification, RMWS dynamically allocates tasks and divides the scheduling process into three stages. First, when a new workflow arrives, the latest start time and the latest finish time of each task are calculated according to the deadline, and the subdeadline of each task is obtained by probabilistic upward ranking. Then, each ready task is allocated according to its subdeadline and the increased cost of the virtual machine (VM). Meanwhile, only one waiting task can be assigned to each VM to reduce delay fluctuations. Finally, when the task is completed on the assigned VM, all the parameters of the relevant tasks are updated before allocating them to appropriate VMs. The experimental results based on four real-world workflow traces show that the proposed algorithm is superior to two state-of-the-art algorithms in terms of total rental cost, resource utilization, success rate and deadline deviation under different conditions.
Hierarchical Disentangled Representation Learning for Outdoor Illumination Estimation and Editing

Data-driven sky models have gained much attention in outdoor illumination prediction recently, showing superior performance against analytical models. However, naively compressing an outdoor panorama into a low-dimensional latent vector, as existing models have done, causes two major problems. One is the mutual interference between the HDR intensity of the sun and the complex textures of the surrounding sky, and the other is the lack of fine-grained control over independent lighting factors due to the entangled representation. To address these issues, we propose a hierarchical disentangled sky model (HDSky) for outdoor illumination prediction. With this model, any outdoor panorama can be hierarchically disentangled into several factors based on three well-designed autoencoders. The first autoencoder compresses each sunny panorama into a sky vector and a sun vector with some constraints. The second autoencoder and the third autoencoder further disentangle the sun intensity and the sky intensity from the sun vector and the sky vector with several customized loss functions respectively. Moreover, a unified framework is designed to predict all-weather sky information from a single outdoor image. Through extensive experiments, we demonstrate that the proposed model significantly improves the accuracy of outdoor illumination prediction. It also allows users to intuitively edit the predicted panorama (e.g., changing the position of the sun while preserving others), without sacrificing physical plausibility.
Cost analysis of reactive power support

The transfer of real power cannot be realized without reactive power/voltage support. However, compared with real power, the idea of reactive power costing and pricing is still not well defined. In this paper, the economic cost of reactive power is first analyzed. This cost includes the explicit and opportunity cost from various generation sources, such as generators, and the explicit cost from various transmission sources, such as reactive compensators, tap-changing transformers and the cost of transmission losses. Different from previous objectives functions, in this paper a cost-based reactive power dispatch is presented, which minimizes the total cost of reactive power support and determines the resultant reactive profile. This cost-based reactive power dispatch methodology provides information on the costing of reactive power and the information may be further expanded to include the pricing of reactive power.
An Efficient Approach Using Knowledge Distillation Methods to Stabilize Performance in a Lightweight Top-Down Posture Estimation Network

Multi-person pose estimation has been gaining considerable interest due to its use in several real-world applications, such as activity recognition, motion capture, and augmented reality. Although the improvement of the accuracy and speed of multi-person pose estimation techniques has been recently studied, limitations still exist in balancing these two aspects. In this paper, a novel knowledge distilled lightweight top-down pose network (KDLPN) is proposed that balances computational complexity and accuracy. For the first time in multi-person pose estimation, a network that reduces computational complexity by applying a “Pelee” structure and shuffles pixels in the dense upsampling convolution layer to reduce the number of channels is presented. Furthermore, to prevent performance degradation because of the reduced computational complexity, knowledge distillation is applied to establish the pose estimation network as a teacher network. The method performance is evaluated on the MSCOCO dataset. Experimental results demonstrate that our KDLPN network significantly reduces 95% of the parameters required by state-of-the-art methods with minimal performance degradation. Moreover, our method is compared with other pose estimation methods to substantiate the importance of computational complexity reduction and its effectiveness.