Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Stochastic EPEC Model for Competition of Dominant Producers in Generation Expansion Planning

This paper aims to presents dynamic stochastic an equilibrium problem with equilibrium constraints (DSEPEC) model to invistigate the generation capacity expansion at a certain time horizon and the presence of dominant producers. The DSEPEC model is proposed while there is an uncertainty in the predicted demand, and it is modeled based on discrete Markov model. Each dominant producer is modeled by a bi-level optimization problem so that the first level and the second level provide models for the investment and operation decisions, respectively. The supply function equilibrium (SFE) is used for short-term electricity market. Then, each bi-level model is convert to dynamic stochastic mathemathical problem with equilibrium constraints (DSMPEC). To convert DSEPEC to an auxiliary MILP problem, Karush-Kuhn-Tucker (KKT) conditions as well as primal-dual transformation. A sample two-buses power network is employed for simulation and necessary analysis to confirm the efficiency of the proposed framework.
Three Degradation Parameters Estimation of a LIB Module Using Single Indicator for In-situ Charge–Discharge Energy Prediction

The co-operation of distributed batteries in the smart grid or virtual power plant is called ‘battery aggregation’. Especially the lithium-ion battery (LIB) is the strong candidate of the aggregated battery. The degradation of the lithium-ion battery causes the increase of charge-discharge energy (CDE) loss and the decrease of economic gain. We studied the charge–discharge energy estimation method for the economic operation of battery aggregation using lithium-ion batteries. The charge–discharge energy of lithium-ion battery is determined by full-charge capacity (FCC), open-circuit voltage (OCV), and internal impedance. We found out the single indicator that can estimate above-mentioned three parameters. In this paper, we discuss about the results of CDE estimation using this single indicator.
Face Detection Using R-FCN Based Deformable Convolutional Networks

The recent years witnessed great improvements in systems of region-based face detection. However, the variations in occlusion, scale, illumination, pose and facial expressions make face detection in the wild still a challenge to be solved. In this paper, a Region-based Fully Convolutional Networks (R-FCN) based deep face detection framework is proposed. Several new techniques are utilized in our framework, including Deformable Convolutional Networks (DCN), Feature Pyramid Networks (FPN) and Focal Loss. Experiment results on three common challenging face detection benchmarks, FDDB, AFW and WIDER FACE, show the proposed approach is robust and performance outperforms most of previous methods, especially for addressing heavy occlusion, part deformation and complex perspective.
Cyber Security Inference Based on a Two-Level Bayesian Network Framework

Graphical models are widely used in cyber security analysis to capture relationships among variables in attack scenarios. However, most models are difficult to build due to the greatly imbalanced data in cyber attacks. To solve this problem, we propose a two-level Bayesian network framework in this paper. We firstly classify the cyber attacks into two levels, one is used to identify general types of attacks and the other is used to classify specific forms. Then we train Bayesian networks for each level. To help administrators cope with threats in time, we propose an analysis method. This method finds the important node's Markov blanket and sorts nodes in it by their influence on each specific form, which could help to understand key threats in cyberspace.
Trademark Design Code Identification Using Deep Neural Networks

Trademark review and approval is a complex process that involves thorough analysis and review of the design components of the marks including the visual characteristics as well as the textual mark description data specifying the significant aspects of the mark. One of the crucial aspect in review of the trademark application is determining the design codes of the trademarks based on their mark description. Currently, the process of identifying the design codes for a trademark is performed manually in the United States Patent and Trademark Office (USPTO) and takes substantial amount of time. Recently, word embeddings and deep neural networks (DNNs) have demonstrated excellent performance in computer vision and various natural language processing (NLP) tasks such as machine translation, speech recognition, sentence and document classification etc. to name a few. In this study, we explored fastText and different neural networks such as Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), bidirectional versions of both LSTM and Gated Recurrent Unit (GRU) and Recurrent Convolutional Neural Network (RCNN) to automate trademark design code classification based on their mark description. Overall, it was found that the trademark word embeddings with RCNN model outperformed other models. Our study thereby seeks to provide a solution towards the time intensive and laborious process of identifying design codes of the trademarks.
ReactSpace: Spatial-Aware User Interactions for Collocated Social Live Streaming Experiences

Live streaming is becoming an increasingly popular medium for social interaction on mobile devices. However, in most cases live streaming experience is still confined to a singleviewpoint, monoscopic video stream, which, in active scenarios such as outdoor or public event broadcasting, makes it hard or even impossible for viewers to understand a remote situation. This study investigates how live stream viewing can be improved through introduction of user spatial data into video stream composition and interaction, by developing a system that allows watching multiple spatially oriented video streams in virtual spaces simultaneously, and interaction with them through voice and spatially distributed reaction buttons. According to results of a preliminary study, users show a statistically significant interest in using spatial live stream viewing and interaction features. Believing that this solution presents a novel approach to social live video streaming, the rest of the article discusses the results and possible extensions of developed system.
Image Reconstruction from Neural Activity Recorded from Monkey Inferior Temporal Cortex Using Generative Adversarial Networks

The generative adversarial network (GAN) is a powerful image generation machine learning model. Several lines of research have shown that GAN is applicable to brain-machine interface technology for deciphering human brain activity, such as EEG and fMRI signals, to visualize what human observers see during recording. However, although current GAN models can synthesize photorealistic images, the quality and variety of image reconstruction from brain activity data recorded by non-invasive techniques are still limited. In this study, we recorded neural spike activities in monkey brain using microelectrode arrays implanted directly on the surface of the inferior temporal cortex, a brain area crucial for visual object recognition. The recorded data were then inputted into a state-of-the-art GAN model (Dosovitskiy & Brox, 2016 [1]) to reconstruct images viewed by the monkey during the experiments. The results showed the advantage of invasive recording methods over non-invasive methods for improving the quality of image reconstruction. The results also demonstrated that the proposed decoding approach is useful in neuroscience research to explore and visualize information represented in the recoding site.
Descriptor-Driven Keypoint Detection

A methodology is proposed (and illustrated on exemplary cases) for detecting keypoints in such a way that usability of those keypoints in image matching tasks can be potentially maximized. Following the approach used for MSER detection, we localize keypoints at image patches for which the selected keypoint descriptor is maximally stable under fluctuations of the parameter(s) (e.g. image threshold, scale, shift, etc.) determining how configurations of those patches evolve. In this way, keypoint descriptors are used in the scenarios where descriptors' volatility due to minor image distortions is minimized and, thus, performances of keypoint matching are prospectively maximized. Experimental verification on selected types of keypoint descriptors fully confirmed this hypothesis. Additionally, a novel concept of semi-dense feature representation of images (based on the proposed methodology) has been preliminarily discussed and illustrated (and its prospective links with deep learning and tracking applications highlighted).
Knowledge Transfer between Multi-granularity Models for Reinforcement Learning

As a widely used machine learning method, reinforcement learning (RL) is a very effective way to solve decision and control problems where learning skills are needed. In this paper, a knowledge transfer method between multi-granularity models is proposed for RL to speed up the learning process and adapt to the dynamic environments. The learning process runs on naturally organized multi-granularity models, e.g., the coarse-grained model and the fine-grained model. This multi-granularity model constitutes a knowledge transfer architecture that bridges the reinforcement learning between different granularity levels. The proposed multi-granularity reinforcement learning (MGRL) approach and related algorithms can scale up very well and speed up learning with other granularity learning process. Several groups of simulation experiments are carried out using a puzzle problem in a gridworld environment. The results demonstrate the effectiveness and efficiency of the proposed approach.
RC-NVM: Dual-Addressing Non-Volatile Memory Architecture Supporting Both Row and Column Memory Accesses

Although emerging non-volatile memories (NVMs) have been comprehensively studied to design next-generation memory systems, the symmetry of the crossbar structure adopted by most NVMs has not been addressed. In this work, we argue that crossbar-based NVMs can enable dual-addressing memory architecture, i.e., RC-NVM, to support both row- and column-oriented memory accesses for workloads with different access patterns. Through circuit-level analysis, we first prove that such a dual-addressing architecture is only practical with crossbar-based NVMs rather than DRAM. Then, we introduce the RC-NVM architecture from bank, chip and module levels, and propose RC-NVM aware memory controller. We also address the challenges to implement the end-to-end RC-NVM system. Especially, we design a novel protocol to solve the cache synonym problem with very little overhead. Finally, we introduce the deployment of RC-NVM for in-memory databases (IMDBs) and evaluate its performance with IMDBs and well-optimized general matrix multiply (GEMM) workloads. Experimental results show that with only 10 percent area overhead 1) the memory access performance of IMDBs can be improved up to 14.5X, and 2) for GEMM, RC-NVM naturally supports SIMD operations and outperforms the best tiled layout by 19 percent.
A 232–1996-kS/s Robust Compressive Sensing Reconstruction Engine for Real-Time Physiological Signals Monitoring

Compressive sensing (CS) techniques enable new reduced-complexity designs for sensor nodes and help reduce overall transmission power in wireless sensor network. However, for real-time physiological signals monitoring, the orthogonal matching pursuit that applied prior CS reconstruction chip designs is sensitive to measurement noise and suffers from a low convergence rate. In this paper, we present a robust 232–1996-kS/s CS reconstruction engine fabricated in 40-nm CMOS. With combination sparsity estimation (SE) and robust subspace pursuit (SP), more than 8-dB signal-to-noise ratio (SNR) gain is achieved under the same success rate for robust reconstruction. For hardware implementation, a flexible indices-updating VLSI architecture inspired by the gradient descent method can support arbitrary signal dimension of CS reconstruction without matrix decomposition. Parallel searching, indices bypassing, and processing elements (PEs) grouping are designed to reduce the total CS reconstruction cycle latency, thus enhancing the throughput rate by approximately 6.3 for CS reconstruction. The 8.66-mm CS reconstruction engine can provide real-time physiological signal reconstruction for data collected from CS-based wireless biosensors under noisy conditions, making low-power patient monitoring a reality.
Rescue Mission Enhancement through Ambulance-to- Vehicle Communications

Rescue missions are very crucial for live saving. The standard implementation of siren signal, such as siren light and sound, could be used to raise awareness of other non-emergency vehicles nearby during the rescue missions. However, there are several limitations of the siren signal. For example, it requires a line of sight to spot any approaching ambulances. However, the eyesight in driving environment is very limited. Siren sound in addition could confuse other non-emergency vehicles, because there is no information in the siren sound. The other vehicles may not be able to know where the ambulances actually are. To enhance the rescue missions, this paper proposes an implementation of vehicular communications to allow communications between ambulances and other non-emergency vehicles called Ambulance-to-Vehicle communications (A2V). A2V contributes in sharing emergency information between ambulances and other vehicles nearby in far advance. Therefore, the other vehicles can know in advance about where exactly the ambulances are, and which route or which lane these ambulances will go through. This information allows other vehicles to clear the emergency route very efficiently even before spotting and hearing the siren light and sound. Our comprehensive performance simulation results and analysis show that A2V remarkably achieves more than 40% speed improvement to ambulance, while rarely reduces speed of the other non-emergency vehicles by less than 6%.
A Cooperative Coevolution Algorithm for the Seru Production With Minimizing Makespan

<italic>Seru</italic> production can be used to enhance productivity, such as makespan reduction and manpower saving. <italic>Seru</italic> system operation includes two NP-hard problems, i.e., <italic>seru</italic> formation and <italic>seru</italic> scheduling. The exact solution cannot be obtained by solving only one of <italic>seru</italic> formation and <italic>seru</italic> scheduling. We develop a cooperative coevolution algorithm for the <italic>Seru</italic> production with minimizing makespan by solving the <italic>seru</italic> formation and <italic>seru</italic> scheduling simultaneously. The cooperative coevolution algorithm includes two evolution algorithms, i.e., the algorithm combining generic algorithm and local search, and the ant colony optimization algorithm. The former algorithm is used to deal with the evolution of <italic>seru</italic> formation. The latter is used to find a better <italic>seru</italic> scheduling. In the cooperative mechanism, the two algorithms cooperate to seek a better solution of <italic>seru</italic> system operation. Finally, extensive-tested experiments show that the proposed cooperative coevolution algorithm can obtain a better solution than all the existing algorithms and, even, can obtain the exact solution for some medium-and-small instances.
Current-mode Temperature Compensation for a Differential Logarithmic Amplifier in 180nm BiCMOS

This paper presents a new approach for the temperature compensation of a diode-based logarithmic amplifier used in on-chip power measurement circuitry. The output voltages of the amplifier are first converted into current domain. Then, the temperature compensation is realized using a translinear circuit and a PTAT current source. As most millimeter-wave systems on chip already contain a PTAT current generator, and the temperature compensation uses only devices readily available in commercial BiCMOS technologies, the presented approach is viable for many different designs. Simulation results of the compensation circuit together with an exemplary log amp show cancellation of the temperature dependence between −40°C and 125 °C with a dynamic range of 50 dB.
Design Space Exploration for Approximate Implementations of Arithmetic Data Path Primitives

While a detailed analysis of various approximation strategies for elementary arithmetic circuits (such as adders and multipliers) is widely covered in the literature, much less is known about approximate arithmetic data path primitives (such as Dot Product (DP) to name one example) because it is difficult to exactly analyze their error and other parameters. We provide a detailed analysis of approximation options if a two-dimensional DP circuit is implemented using pre-characterized approximate arithmetic circuits available in existing libraries of approximate circuits. We propose a method capable to cheaply estimate properties of candidate approximate implementations of DP circuit. By a careful selection of approximate components, hundreds of approximate DP circuits showing excellent tradeoffs between key design objectives were obtained.
Miniaturized onboard electronics for attitude measurement of medium caliber projectiles

A part of ISL's research activities includes aeroballistics studies of new projectile architectures which require embedded instrumentation in large and medium caliber models. This paper describes a miniaturized 16 mm diameter on-board telemetry and sensor block specifically designed for the studies of the new ISL Long Range projectile, scaled down from 155 mm to 25 mm caliber to allow free-flight testing with a 105L33 smooth bore launcher. To keep the whole telemetry instrumentation system compact, a PCB design and a new concept of a miniaturized antenna will be presented as well as the mechanical structure realized to provide sufficient strength to survive the high g-forces during launch and soft recovery. Finally, experimental setup conditions and acquired data is presented as results.
2.5D Faster R-CNN for Distance Estimation

Estimating the distance of a target object from a single image is a challenging task since a large variation in the object appearance makes the regression of the distance difficult. In this paper, to tackle such the challenge, we propose 2.5D anchors which provide good candidates of distances, based on a perspective camera model. This candidate is expected to relax the difficulty of the regression model since only the residual from the candidate distance needs to be taken into account. We show the effectiveness of our proposed anchors, by comparing with ordinary regression methods, through experiments with Pascal 3D+ TV monitor dataset and Tsukuba challenge task.
A Low Voltage Ride Through Scheme for DFIG-Based Wind Farm With SFCL and RSC Control

Taking into consideration the low voltage ride through (LVRT) demand proposed in grid codes, a resistive-type superconducting fault current limiter (R-SFCL) LVRT solution is presented in this paper for doubly fed induction generator (DFIG) based wind turbine generators. The installed position, equivalent modelling of R-SFCL, and technical principle for improving LVRT capability are described. Cooperative control between R-SFCL and two advanced control strategies—reactive power control (RPC) and inductance-emulating control (IEC)—are introduced in this paper to help the DFIG generate reactive power and protect key parameters during grid faults. Simulations and performance evaluation are also conducted compared with the single IEC scheme and single RPC-IEC scheme, which demonstrate the effectiveness and superiority of the proposed scheme in enhancing LVRT capability.
Impact of Near-Threshold and Variability on 7nm FinFET XOR Circuits

This paper evaluates ten different XOR logic gates arrangements behavior at near-threshold operation under process, voltage, and temperature (PVT) variability effects. The experiments adopt the 7nm FinFET High Performance and Low Standby Power technologies predictive models. At nominal voltages, high-performance devices show superior robustness for PVT variability, except to the influence of temperature over static current. However, near-threshold (NT) computing is a good option when lower power consumption is needed. According to results, the behavior of NT circuits under PVT variability should be highlighted, as it obtained lower values and more robustness than nominal approaches. Voltage variations inferior to 160 mV could provoke from 15% to 70% oscillation on delay. But, the same voltage variation could affect dramatically the total power deviations by 188% to 434%. Considering the different operation conditions explored in this work, the results provide valuable data and how the impact of variability is an important factor that must be explored to design more robust circuits.
A Novel Sizing Method Aiming Security Against Differential Power Analysis

The confidential information provided by embedded cryptosystems are threatened by Side Channel Attacks (SCA). Differential Power Analysis (DPA) is an SCA that correlates the dependency between processed data and the system's power consumption in order to obtain the secret information. Circuit-level countermeasures against DPA aim at achieving the data-independent supply current. This work proposes a sizing methodology focused on equalizing the power dissipation of logic gates towards increasing security against DPA attacks. The developed algorithm searches for an optimal sizing by iteratively expanding the solution scope and verifying its consistency through SPICE simulations. Topologies used as countermeasures to DPA attacks were submitted to the proposed methodology and their security metrics were evaluated. Results from DPA attacks show that circuits sized with the proposed method had their keys less likely to be broken for all selection functions and key searches tested.
An Improved Observer-based Integral State Feedback (OISF) Control Strategy of Flyback Converter for Photovoltaic Systems

An improved robust control strategy is proposed in this work to deal with the output regulation control for a DC–DC flyback converter in photovoltaic (PV) system. The control design consists of an observer-based integral state feedback controller (OISF) combined with a standard optimization algorithm. Thus, a PSO algorithm is applied to optimize the OISF controller parameters giving optimal controller gains. In addition, the designed OISF-based PSO algorithm permits to regulate the PV system input voltage such that the PV system operates at maximum power point (MPP). The simulation results obtained under different scenarios clearly demonstrate the effectiveness and improved performance obtained by the proposal (OISF-PSO) scheme compared with conventional PI-based PSO and LQRbased PSO controllers. Also, this improved approach ensures faster tracking of reference maximum power voltage provided by MPPT with a satisfactory performance.
A Decentralised Control Algorithm for an Automated Coexistence Management

Several wireless communication systems operating in parallel are typical for industrial applications. These systems have to be coexistent in order to fulfil all their application communication requirements. Ensuring this coexistence is called coexistence management. In this contribution we investigate a decentralised control approach for an automated coexistence management. Here we use a model predictive control approach in max - plus algebra for timed Petri-net methods. We show a theoretical stability proof of the controller to the reference value. For that, we convert the original coordinates into error coordinates. Finally, we use a practical hardware-in-the-loop setup with WiFi-technology as test system for validation the controller.