Miracle-dz/UniFAR-FTUs huggingface dataset:
MulSim: A Novel Similar-to-Multiple-Point Clustering Algorithm

Finding clusters in datasets with different distributions and sizes is challenging when clusters are of widely various shapes, sizes, and densities. Based on a similar-to-multiple-point clustering strategy, a novel and simple clustering algorithm named MulSim is presented to address these issues in this paper. MulSim first defines a new distance which can automatically adapt different densities when clustering. Then, the MulSim groups two points together if and only if one point is similar to another point and its similar neighbors. Our comprehensive experiments on both multi-dimensional and two dimensional datasets representing different clustering difficulties, show that the MulSim performs better than classical and state-of-the-art baselines in most cases. Besides, when increasing the size of datasets, MulSim can still ensure good clustering quality. In addition, the impact of the two MulSim parameters on clustering quality as well as the way of the parameter estimation are analyzed. In the end, the practicability and feasibility of the algorithm are tested through a face recognition example.
Looped Network Oriented Distributed Power Routing Method with the Operation of Phase Shifters

The deregulation of power industry enables large power users and power producers to trade power directly between different regional markets, making it important for market participants to conduct research on the method to find out the best route for power trading. Besides, as the coordinator between the regional markets have limited access to the information of every market, the power routing problem should be solved in a decentralized way. In this paper, we propose a looped network oriented distributed power routing method with the operation of phase shifters (PSs), which extends our previous work on the power routing method in loop-free networks. We transforms the optimization model for the lowest transaction cost to a minimal path problem and applies Bellman-Ford algorithm to solve it. To control the power flow of tie lines, we utilize phase shifters (PSs) and develop a method to determine their reasonable set points in a distributed way. A six-market case with two loops were used to illustrate the efficiency of the method.
Big Data Forex Analysis using GPU Computing

The largest financial market in the world is the Forex (Foreign Currency Exchange) market, by virtue of the highest volume of trading that takes place on a daily basis. Being able to read underlying market patterns and making smart choices amidst the turbulent and organic Forex marketplace is the first step to decision making. Traders and investors harvest profitable returns from Forex market by buying and selling when the exchange rates are respectively low and high. Indicator analyses can be used to locate the ideal times to convert back and forth between currencies. These Indicator analyses themselves involve parameters, which are usually chosen manually from experience, which usually are not the optimal choices. The parameters can be optimized from historic data using software, but this is computationally intensive and time consuming. In this paper, we propose a method to speed up the optimization of indicator parameters, using CUDA parallel processing API of NVIDIA GPUs (Graphical Processing Units) as opposed to the classic CPU based sequential approach. While it seemed logical to incorporate several high-end processors (CPUs) in order to harness more computing power, we aim at demonstrating that a GPU based implementation, based on suitably written kernels and threads, has the potential to be scaled for industrial use.
A Resonant Gate Driver for Silicon Carbide MOSFETs

2In this paper, a resonant gate driver for silicon carbide power MOSFET is proposed. This resonant gate driver contains four N-MOSFETs, a resonant inductor, and a capacitor. The proposed gate driver recycles the energy which is stored in the gate capacitor of the SiC MOSFET. The gate drive losses of the resonant gate driver are reduced greatly, and the switching frequency can reach MHz. The design and the loss analysis are introduced in this paper. Finally, the simulation model and the experimental platform of the resonant gate driver are built to validate the theoretical analysis. Both the simulation and experiment results are provided to verify the feasibility and high performance of the proposed resonant gate driver.
Consensus-Based Droop Control of Isolated Micro-Grids by ADMM Implementations

Due to low inertia, high penetration of renewable generators in isolated micro-grids may result in poor frequency and voltage response under large disturbances. In order to overcome this difficulty, the distributed average consensus algorithm has been studied recently by extending local droop control mechanisms of power converters into distributed droop control. However, its convergence rate and the resilience to noises are still not satisfactory. In this paper, two implementations of consensus-based distributed droop control by the Alternating Direction Multipliers Method (ADMM) are proposed. It can be shown that the closed-loop system is described by the second-order vector differential equation, and the stability of every trajectory can be analyzed by the energy function approach under certain mild conditions. Real-time digital simulations are performed to validate the effectiveness of the proposed distributed droop control mechanism.
A Stochastic Energy Management System for Isolated Microgrids

Energy management in isolated microgrids is an important task since they have limited generation capacity and are expected to rely on various uncontrollable supply resources to match and balance the demand. While plug-in electric vehicles (PEVs) present a promising solution to reduction of greenhouse gas emissions, their increasing penetration impacts the system operation, particularly in isolated microgrids. Therefore, PEV load management is an important issue. Similarly, demand response (DR) has the potential to provide significant flexibility in the operation of isolated microgrids with limited generation capacity, by altering the demand and introducing an elasticity effect. This paper presents a stochastic energy management system (EMS) model for isolated microgrids considering PEVs and DR, for several probabilistic operational scenarios in short-term dispatch. The proposed stochastic EMS model accounts for the uncertainties in wind and solar generation, energy consumption patterns of customers, and the stochastic nature of the state of charge (SOC) of PEV batteries at the start of charging.
Low-cost, Implantable Wireless Sensor Platform for Neuromodulation Research

The role of peripheral nerves in regulating major organ function in health and disease is not well understood. Elucidating the relationships between biomarkers and neural activity during conditions free form anesthesia is essential to advancing future investigations of autonomic organ control and improving precision for neuromodulation treatment approaches. Here we present a simple, customizable, off-the-shelf component sensor platform to meet research needs for studying different organs under conscious, free movement. The platform consists of a small, rechargeable coin-cell battery, an energy-harvesting IC, a low-power microcontroller, a low-power pressure transducer, customizable number of electrodes with a common anode, inductive recharge input, and OOK inductive transmission. A case study demonstrating a bladder implant for long-term monitoring is presented, utilizing a novel, non-hermetic encapsulation approach. The customized platform uses two sleep modes to minimize battery loading, exhibiting a maximum time-averaged current draw of 125 micro-amps during sensing and transmission, with a quiescent current draw of 95 nano-amps into the microcontroller.
High Temperature Annealing of ZnO:Al on Passivating POLO Junctions: Impact on Transparency, Conductivity, Junction Passivation, and Interface Stability

We investigate the enhancement in transparency and conductivity of aluminum doped zinc oxide (ZnO:Al) layers upon high-temperature annealing and its impact on contact resistance, as well as, on passivation properties of carrier selective junctions based on doped polycrystalline Si on a passivating silicon oxide (POLO). The temperature stability of these junctions allows annealing of the ZnO:Al/POLO combination up to 600 °C. We prepare ZnO:Al films by dc magnetron sputtering at room temperature. We determine the complex refractive index of ZnO:Al in dependence of post-deposition annealing (PDA) temperature by spectroscopic ellipsometry. High-temperature annealing improves the conductivity and reduces the absorption within ZnO:Al. The optical losses in a ZnO:Al/POLO stack are rather limited by the poly-Si layer than by the ZnO:Al. The sheet resistance improves from roughly 20 000 Ω/sq for 80 nm thick as-deposited ZnO:Al films to 72 Ω/sq after fast firing at 600 °C. At the same time, PDA cures the damage induced in the POLO junctions during ZnO:Al deposition. After PDA with AlxOy capping layers, the passivation quality even surpasses the initial level. A transmission electron microscopy analysis of the interface between the ZnO:Al and the underlying poly-Si reveals the formation of a silicon oxide like interfacial layer after PDA at 400 °C. This interfacial layer causes a high contact resistivity of the metal/ZnO:Al/POLO-junction and could limit the thermal budget for cell processing. Our results indicate that after successful process adjustment, ZnO:Al could substitute In-based transparent conductive oxides on POLO cells for cost reasons, as well as, enable a high efficiency potential.
lightMEC: A Vendor-Agnostic Platform for Multi-access Edge Computing

Multi-access Edge Computing promises to improve mobile users experience by bringing storage and processing capabilities to the edges of the network. This approach allows third parties to deploy innovative services, like augmented reality, directly in the radio access network paving the way for new business models and new revenue stream for mobile network operators (MNOs). In this paper, we introduce lightMEC a lightweight solution for deploying mobile edge computing functionalities which allows hosting of low-latency and bandwidth-intensive applications at the network edge. Measurements conducted over a real-life test demonstrated that lightMEC can actually support practical MEC applications without requiring any change to the functionality of existing mobile network nodes both in the access and the core network segments. The significant benefits of adopting the proposed architecture are analyzed based on a proof-of-concept demonstration of the content caching use case.
A Balanced Feed Filtering Antenna With Novel Coupling Structure for Low-Sidelobe Radar Applications

A fourth-order filtering patch antenna with a novel coupling structure is presented in this paper. Using the proposed coupling structure, both the balanced coupling feed and cross-coupling are realized. Two identical slots etched on the ground plane are utilized to excite the radiating patch with the reduced cross-polarization level. A short slot etched on the ground plane is employed for cross-coupling, which introduces two controllable radiation nulls with a steep roll-off rate. In addition, owing to the split-ring resonators and hairpin resonators, the improved impedance bandwidth is achieved with the fourth-order filtering response. To demonstrate the proposed design techniques, both the filtering antenna element and the low-sidelobe array are designed, fabricated, and measured. The measured results show that the proposed antenna has the impedance bandwidth of 12% (4.78–5.39 GHz) with the total height of $0.06\lambda _{0}$ , the cross-polarization level lower than −31 dB, and two radiation nulls with the suppression higher than 31 dB. For the low-sidelobe antenna array, wide impedance bandwidth is also obtained with the sidelobe level below −28.7 dB, the cross-polarization level below −34 dB, and the out-of-band suppression better than 25 dB.
Selection Based on Colony Fitness for Differential Evolution

Differential evolution (DE) is a competitive and reliable computing technique for continuous optimization. A diversity-based selection has been proved to be valid to improve the performance of DE. However, further study can be done. In this paper, we propose two versions of colony fitness, fitness with the consideration of diversity information. Selection based on the first version of the colony is embodied in DE/rand/1, a basic DE algorithm, while selection based on the second version is used in CoBiDE, a state-of-the-art DE algorithm. Our experiments are based on the 2005 Congress on Evolutionary Computation and the 2014 Congress on Evolutionary Computation benchmark functions. Experimental results show that our modification on algorithms leads to significantly better solutions than before.
Temporal Difference Learning Based Critical Component Identifying Method with Cascading Failure Data in Power Systems

In order to mitigate the risks of cascading failures in power systems, the critical faults that lead the system to severe load losses should be identified. Since piles of fault data containing the failure features have been stored during system operations, it is drawing increasing attention to leverage the data for criticalness analyses in recent years. This paper proposes a Temporal Difference learning (TD-learning) based method to identify critical component faults by exploring cascading failure fault chain data. The TD-learning method can allocate the final load losses to faults at different stages so that the relevance of the faults to severe blackouts can be derived. Eventually, the faults allocated larger losses are suggested to be more relevant to severe blackouts, i.e., these faults are more critical. At the same time, the precedent faults putting the critical components in jeopardy can be easily found, which uncovered the preconditions of component criticalness. The method is validated on the IEEE 14-bus system, and a considerable decrease of blackout risks is achieved by enhancing the critical components identified by this method.
Data Mining Based Partitioning of Dynamic Voltage Control Areas and Contingency Clustering

Partitioning of dynamic voltage control areas (DVCAs) and contingency clustering have attracted increasing attentions in power system planning. In this paper, we propose a data mining based method to recognize behavior patterns of buses and contingencies from offline simulation, so as to identify DVCAs and group contingencies. The voltage control ability index (VCAI) is defined firstly to evaluate the control effect of a bus with VAR injection subject to a contingency. By traversing all the influencing factors of VCAI, including contingency, controlling bus, and observed bus, a data pool of VCAI is obtained. Behavior patterns of bus and contingency are then extracted from the data pool, respectively. Similarity metric for behavior pattern is defined and the affinity propagation clustering algorithm is adopted to cluster buses and contingencies, so as to form DVCAs and contingency clusters, respectively. Silhouette coefficient analysis is applied to determine a proper clustering scheme. The proposed approach is tested on a modified NE 39-bus system to validate its effectiveness.
Fault Diagnosis of Power System Considering Communication System Information Transmission Error

The existing fault diagnosis systems of power grids are subject to uncertainties in fault information, such as transmission errors in communication channels, thus causing poor practical application. To address these problems, in this paper, we describe an information model for fault diagnosis based on a real communication channel that combines with an expert system using information theory. Here the ‘traditional’ expert system based on strict rule-matching was modified to a numerical computation method, whereby the process of diagnosis is not influenced significantly by signal chronology; this allows information transmission uncertainties to be described quantitatively. The fault diagnosis system for a power grid based on the algorithm proposed was applied successfully in a practical area control center. Our results showed that this method can solve ambiguities in information and the uncertainties of real communications. Despite interference in telecontrol communications, the diagnostic results given by the proposed method were reasonable.
Improving Transfer Learning and Squeeze- and-Excitation Networks for Small-Scale Fine-Grained Fish Image Classification

Scientific studies on species composition and abundance distribution of fishes have considerable importance to the fishery industry, biodiversity protection, and marine ecosystem. In these studies, fish images are typically collected with the help of scuba divers or autonomous underwater vehicles. These images are then annotated manually by marine biologists. Such a process is certainly a tremendous waste of manpower and material resources. In recent years, the introduction of deep learning has helped making remarkable progress in this area. However, fish image classification can be considered as fine-grained problem, which is more challenging than common image classification, especially with low-quality and small-scale data. Meanwhile, well-known effective convolutional neural networks (CNNs) consistently require a large quantity of high-quality data. This paper presents a new method by improving transfer learning and squeeze-and-excitation networks for fine-grained fish image classification on low-quality and small-scale datasets. Our method enhances data augmentation through super-resolution reconstruction to enlarge the dataset with high-quality images, pre-pretrains, and pretrains to learn common and domain knowledge simultaneously while fine-tuning with professional skill. In addition, refined squeeze-and-excitation blocks are designed to improve bilinear CNNs for a fine-grained classification. Unlike well-known CNNs for image classification, our method can classify images with insufficient low-quality training data. Moreover, we compare the performance of our method with commonly used CNNs on small-scale fine-grained datasets, namely, Croatian and QUT fish datasets. The experimental results show that our method outperforms popular CNNs with higher fish classification accuracy, which indicates its potential applications in combination with other newly updated CNNs.
Protection Configuration and Scheme for the Transmission Line of VSC-HVDC Grid

Voltage-sourced converter(VSC) based high voltage direct current(HVDC) grid can absorb large-scale renewable energy flexibly. But because of its small inertia, a fault in the VSC-HVDC grid will spread rapidly and fault current will increase quickly. Because it is hard for DC circuit breakers to cut off large current, the DC transmission line protection has to operate extremely fast after a fault occurs in order that DC circuit breaker can cut off the fault current before it is too large. This paper discusses the structure and configuration of the DC transmission line protection for VSC-HVDC grid. The existing DC transmission line protection are discussed, and the problems and challenges that DC transmission line protection faces are pointed out.
A New Technique Using Tunnel Shape Information to Improve Path Search in Detailed Routing

One approach to solve detailed routing is net-bynet using a fast A*-based path search algorithm to handle long connections. The path search is restricted to certain areas imposed by the global router, which we call tunnels. Since a tunnel forces the path to follow its shape, a tunnel with many detours produces a path whose cost is far higher than the distance. This makes the path search algorithm to suffer heavy performance penalties. Thus, this work proposes new methods to preprocess the tunnels and use the gathered information during the search, providing a more realistic lower bound for the heuristic function of the path search algorithm. The experiments compared Hetzel's algorithm [1] using distance and our method. The results showed a time improvement of 64% in average.
Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain

Surface electromyography (sEMG) waveforms are widely used to generate control signals in several application areas, ranging from prosthetic to consumer electronics. Classically, such waveforms are acquired at Nyquist rate and digitally transmitted trough a wireless channel to a decision/actuation node. This causes large energy consumption and is incompatible with the implementation of ultra-low power acquisition nodes. We already proposed Compressed Sensing (CS) as a low-complexity method to achieve substantial energy saving by reducing the size of data to be transmitted while preserving the information content. We here make a significant leap forward by showing that hand movements recognition task can be performed directly in the compressed domain with a success rate greater than 98 % and with a reduction of the number of transmitted bits by two order of magnitude with respect to row data.
An Energy Efficient Communication Protocol for Low Power, Energy Harvesting Sensor Modules

Energy consumption has become one of the main parameters in evaluating sensor systems in the IoT market. Long range sensor systems are not an exception. Their application in the outdoor environment makes them an ideal candidate to combine energy harvesting mechanisms with the low power features. Using just low power electric components is not enough to ensure energy efficiency. Techniques and mechanisms for harvesting and saving energy are presented in this paper and in its core, a low power, energy efficient communication protocol is described. The proposed protocol optimizes the way in which information is collected from the environment, packed and transmitted over long distances with minimum energy. It is specially designed for energy constrained sensor modules which rely on energy harvesting. The gathered information is transmitted in a form of two different packet types called Teach-in and Data telegrams, respectively. A detailed structure of these packets and the energy required for transmission is explained in this paper, along with a deeper protocol physical layer analysis. In addition, this paper demonstrates the huge potential for mass market, as a result of the many proposed solutions that have been tested and implemented in a fully developed functioning prototype.
Design and Evaluation of a III–V/Si Partial CPV Module for Maximization of Power Generation per Unit Module Area

This paper describes the design and evaluation of a III–V/Si partial concentrator photovoltaic (CPV) module with a stacked structure comprising a highly transparent CPV module and a Si cell, which aims to maximize the power generation from global normal irradiation (GNI) by harvesting not only direct, but diffuse sunlight as well. The module structure was designed by considering the optical and heat transfer characteristics, and its performance was evaluated by outdoor and indoor tests using a prototype submodule with a geometrical concentration ratio of 100×. The submodule achieves diffuse sunlight transmission of over 80%, thus generating more power from diffuse sunlight. Under clear-sky condition, the submodule with a single-sided Si cell exhibits the maximum GNI-based module efficiency of 30.7%. The submodule with a bifacial Si cell further improves the power generation and tolerance to tracking error angle for various sunlight conditions.