Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Current Probabilistic Power Flow Calculation Based on Cumulated Method

The probabilistic load flow distribution of power system is calculated based on Cornish-Fisher expansion, which constructs the immediate relationship between unknown and known variables with the cumulated as a link and takes a deterministic load flow calculation as the starting point to generate the probabilistic load flow distribution function with high accuracy. Simulative results show that, compared with the Monte Carlo simulation sampling method, the proposed method enhances the computational speed significantly while maintains the computational accuracy.
CD-Xbar: A Converge-Diverge Crossbar Network for High-Performance GPUs

Modern GPUs feature an increasing number of streaming multiprocessors (SMs) to boost system throughput. How to construct an efficient and scalable network-on-chip (NoC) for future high-performance GPUs is particularly critical. Although a mesh network is a widely used NoC topology in manycore CPUs for scalability and simplicity reasons, it is ill-suited to GPUs because of the many-to-few-to-many traffic pattern observed in GPU-compute workloads. Although a crossbar NoC is a natural fit, it does not scale to large SM counts while operating at high frequency. In this paper, we propose the converge-diverge crossbar (CD-Xbar) network with round-robin routing and topology-aware concurrent thread array (CTA) scheduling. CD-Xbar consists of two types of crossbars, a local crossbar and a global crossbar. A local crossbar converges input ports from the SMs into so-called converged ports; the global crossbar diverges these converged ports to the last-level cache (LLC) slices and memory controllers. CD-Xbar provides routing path diversity through the converged ports. Round-robin routing and topology-aware CTA scheduling balance network traffic among the converged ports within a local crossbar and across crossbars, respectively. Compared to a mesh with the same bisection bandwidth, CD-Xbar reduces NoC active silicon area and power consumption by 52.5 and 48.5 percent, respectively, while at the same time improving performance by 13.9 percent on average. CD-Xbar performs within 2.9 percent of an idealized fully-connected crossbar. We further demonstrate CD-Xbar's scalability, flexibility and improved performance per Watt (by 17.1 percent) over state-of-the-art GPU NoCs which are highly customized and non-scalable.
Dynamic Multimodal Optimization: A Preliminary Study

The benchmark problems have played a fundamental role in verifying the algorithm’s search ability. A dynamic multimodal optimization (DMO) problem is defined as an optimization problem with multiple global optima and characteristics of global optima which are changed during the search process. Two cases are used to illustrate the application scenario of DMO. A set of benchmark functions on DMO, which contains eight problems, are proposed to show the difficulty of DMO. The properties of the proposed benchmark problems, such as the distribution of solutions, the scalability, the number of global/local optima, are discussed.
A First-Principles Investigation of Boron Nitride Nanotube (BNNT) in Gas Sensing Application

This paper has investigated the adsorption effect of boron nitride nanotube (BNNT) for CO, CO2, NO, NO2, NH3 and H2 molecules based on the first-principle calculations. The potential application of BNNT as highly sensitive gas sensor for CO2, NO, NO2 and NH3 molecules has also been demonstrated. CO2, NO, NO2 and NH3molecules chemically adsorb on BNNT with large adsorption energies due to strong chemical bonds. While CO and H2molecules adsorb weakly on BNNT with lower adsorption energy. The NO2, NH3, and H2molecules are found to donate charge to BNNTs, while CO, CO2 and NO molecules accept charge in the case of BNNTs. In addition, these calculations show that recovery time increases with the adsorption energy, but decreases with the increase of temperature.
Design of Optimal Ship Steering Active Disturbance Rejection Controller Based on Adaptive Particle Swarm Optimization

In this paper, a novel active disturbance rejection controller (ADRC) of ship steering control is designed under the internal parameter uncertainties and external disturbances. The ship heading control model is rather a complex model with large time delay, uncertainties and serious external disturbances, which may result in poor control performance. Therefore, ADRC is applied to control the steering of the ship. However, manually tuning the parameters of the ADRC is timeconsuming and labor-intensive. An optimization method based on adaptive particle swarm optimization (APSO) to adjust the ship’s steering ADRC parameters is proposed. In the developed APSO, considering the control performance, the fitness function is modified in the light of the integrated time absolute error (ITAE) criteria. Then the inertia weight of the previous velocity in the current velocity update is varied based on the fitness value of each individual at the previous moment. Finally, the effectiveness of the proposed adaptive optimization method is proved by simulation experiments.
Passenger Searching from Taxi Traces Using HITS-Based Inference Model

Passenger-searching strategies, as the crowd intelligence of massive taxi drivers, are hidden in their historical GPS traces. Mining traces to understand the efficient passenger searching strategies can benefit drivers themselves. Traditionally, the research on passenger search strategies from taxi GPS traces is mainly focused on statistical techniques. Although this can improve the ability of drivers to find potential passengers through hotspots recommendation, most of these research still directly use raw GPS data and failed to take drivers' experience into account. Moreover, because driver's experience is behind of raw data, can't obtain directly, so the traditional model is unable to make good use of it during hot spots mining. In this paper, we proposed an inference model based on HITS (Hypertext Induced Topic Search), which perfectly describes the relationship between hot spots and drivers' experience and thus effectively handle the above problem. We first extract hotspots by an innovative PDBSCAN algorithm based on fuzzy grid partition and match them with corresponding landmarks by a landmark matching algorithm which based on kernel density estimation. Then the HITS-based inference model is used to mine popular hotspots and the most experienced drivers. Finally, we plan an optimal path for drivers. Experimental results demonstrate the efficiency and the ability of this method to provide drivers with better hotspots and hunting sequences recommendation.
Human Body Action Recognition Based on Bone Information by Kinect

This paper has proposed a method to identify human body action by the 3D data of human skeleton obtained by Kinect depth camera. Using this method, the human action can be identified by utilizing the difference between the angles of bone points from different human body actions. The experiment results show that the method is feasible and efficient, which can offset the influence of complex background and different light intensity.
A Study on Online Hyper-heuristic Learning for Swarm Robots

Swarm robots continue to become more prominent in solving challenging tasks in real world applications. Due to the complexity of operating in often unknown environments, centralised control of swarm robots is not ideal. Prior manual programming is also not practical under these kind of circumstances. Thus, we establish a hyper-heuristic based learning approach for swarm robot control. With this framework, robots can autonomously identify appropriate heuristics from a set of given low-level heuristics, each heuristic guiding certain behaviours. We evaluated this type of online learning on building surface cleaning and studied the effectiveness of our hyper-heuristic online learning. Nine heuristics were proposed in this study. Through the experiments it can be seen that robots can improve their cleaning performance through the online learning process. More importantly, the experiments show that appropriate heuristics can be selected even when the size of the heuristic set is changed. The study on four types of environments shows that with the same heuristic set, the robot swarm can adapt to different environments for different tasks. Hence, hyper-heuristic learning is an effective method for decentralised control of swarm robots.
Hierarchical Optimal Control of Electric Vehicle Considering Wind Power Consumption

In order to cope with the large-scale electric vehicles (EVs) access to the grid, a hierarchical optimization model of distribution network-electric vehicle centralized control center is established by using the way of EVs zonal cluster. The charging and discharging plan of each centralized control center is formulated by the distribution network layer. Considering the safety of the distribution network and the power demand constraints during the control cycle of EVs, the objective is to minimize the load variance and network loss of the distribution network after the access of distributed wind power. According to the charge and discharge plan, the centralized control center carries out rolling optimization control for EVs in their respective areas. The NSGA-II algorithm is used to solve the multi-objective optimization problem of the distribution network layer. The centralized control center performs the orderly charge and discharge control by calculating the priority of charging and discharging. Finally, the improved IEEE-33 node system with 3 distributed wind power and 3 centralized control centers is used as an example for simulation, and the results verify the effectiveness of the proposed model and method.
A Novel CMA-ES with an Eigen Coordinates Framework for Parameter Identification in Photovoltaic Models

The parameter estimation of a photovoltaic (PV) model that measures the change of the current-voltage dataset is a key issue for PV system applications. It is regarded as a complex multimodal optimization problem, and there have been several attempts to solve the problem in this field of research. To further address this problem in a faster and more accurate manner, we demonstrate an improved covariance matrix adaptive evolutionary strategy (CMA-ES) with novel modifications in an eigen coordinates framework named EC-CMA-ES. The original CMA-ES suffers from premature convergence and has poor exploration performance. Thus, we propose an eigenvalue adjustment strategy on a covariance matrix in order to drive evolution towards the dominant domain by adjusting its eigenvalues. Moreover, we perform a local search strategy in a later stage by utilizing the neglected inferior solutions to enrich the population diversity. We apply EC-CMA-ES to address the parameter identification of three commonly used PV models. The statistical results and comparisons with other state-of-the-art algorithms demonstrate the competitive performance of our modified CMA-ES in terms of efficiency and accuracy.
Anonymization in Online Social Networks Based on Enhanced Equi-Cardinal Clustering

Recent trends show that the popularity of online social networks (OSNs) has been increasing rapidly. From daily communication sites to online communities, an average person’s daily life has become dependent on these online networks. Hence, it has become evident that protection should be provided to these networks from unwanted intruders. In this paper, we consider the data privacy on OSNs at the network level rather than the user level. This network-level privacy helps us to prevent information leakage to third-party users, such as advertisers. We propose a novel scheme that combines the privacy of all the elements of a social network: node, edge, and attribute privacy by clustering the users based on their attribute similarity. We use an enhanced equi-cardinal clustering (ECC) as a way to achieve <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-anonymity. We further improve <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-anonymity with <inline-formula> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula>-diversity. Our proposed enhanced ECC ensures that there are at least “<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>” users in any given network as well as the attributes in each cluster has at least <inline-formula> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula>-distinct values. We further provide proofs on how the proposed ECC ensures <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-anonymity and the maximum information loss. We consider a weighted directed social network graph as an input to our method to consider the existing complexities in a social network. With the help of two real-world data sets, we evaluate this method in terms of privacy and efficiency.
Textile Touch-Sensor Array based on Circular and Non-Circular Polymer Optical Fibers

Textiles and flexible textile surfaces can be found in many industrial goods. Due to trends like smart textiles and consumer electronics touchpads and flexible touch-sensitive sensor arrays that can be integrated in these textile surfaces are increasingly in demand. A multi-touch capable textile touchpad based on polymer optical fibers (POF) is presented. For the sensing fibers, different fiber cross sections – from circular to trilobal – are studied for maximum sensitivity and minimum cross talk. Results show that the trilobal cross section improves the sensitivity and the strength of the sensor signal by about 50%.
A 500-Mbps Digital Isolator Circuits using Counter-Pulse Immune Receiver Scheme for Power Electronics

A 500-Mbps digital isolator with the isolation voltage more than 1kV is described, which is the fastest high-voltage isolator ever reported. The high-speed feature is achieved by a newly proposed Counter-Pulse Immune Receiver (CPIR) scheme based on a cross-coupled Schmitt-trigger circuit. The isolator is manufactured in a widely-used high-voltage 0.18-μm CMOS technology and thus can be integrated with smart digital driver circuits for power electronics.
Improved Liquid Source Vaporization for CVD & ALD Precursors

Many of the advanced gas-phase processes used in state-of-the art microelectronic fabrication place higher demands on liquid vapor delivery solutions. Vaporization challenges include a diverse range of liquids with unique material properties, the use of liquids with low vapor pressure or the use of liquids with a small window between thermal decomposition and vaporization. The growing implementation of short pulse processing also creates a need for faster response times. The Performance Enhanced Turbo-VaporizerTM Liquid Delivery System presents a new alternative for liquid vaporization.
TeaStore - A Micro-Service Reference Application

Micro-service architectures are increasingly adopted in industry as they offer high scalability, availability and development speed. The research community has proposed a variety of approaches to automate resource scaling, placement decisions, failure recovery, parameter tuning and many other tasks enabling the self-management of micro-services. Evaluating these approaches in a realistic scenario requires a reference application that offers a range of different behaviors, as well as the necessary degrees of freedom. Existing reference applications either build on an outdated technology stack, do not implement a micro-service architecture or do not have a realistic performance behavior. Production software can usually not be used to evaluate research methods as researchers rarely have access to production software. In this demonstration we showcase the TeaStore, a micro-service reference application with a state-of-the-art technology stack. The TeaStore consists of five services with diverse performance behavior and can be used to evaluate the applicability of novel self-management approaches for micro-services and autonomic software systems in general. We give an overview of the TeaStore architecture, show different deployment options, describe how to run different load profiles and lastly how to collect monitoring data.
Two-Metric Helper Data for Highly Robust and Secure Delay PUFs

The Physically Unclonable Function (PUF) has become an inescapable security primitive, as it generates a fingerprint unique to each device, and is natively robust against reverse engineering attacks. However it suffers from many flaws due to a native lack of reliability and sensitivity against modeling and physical attacks. In the state-of-the-art, the enhancement of reliability involves the use of a helper data which takes advantage of Error-Correcting Codes (ECC), situated after the entropy source, thus increasing the overall PUF complexity and creating leakages exploited by Side Channel Attacks.This paper presents a new method to generate Helper Data, situated before the entropy source, which does not use ECC but still improves drastically the reliability of the PUF. It also provides high entropy and proven resistance against the attacks on the helper data. This method applies to "strong" delay PUFs used as master key generators, thus unsensitive to modeling attacks (the PUF responses are never disclosed). The validation has been done on real ASIC devices fabricated in CMOS 45 nm technology and shows that the reliability can be as low as 10−8 of Bit Error Rate.
Wireless Power Transmission Powering Miniaturized Low Power IoT devices: A Revie

Energy Harvesting (EH) is a mature solution to extend the short lifetime of many wireless sensor nodes and IoT devices. However, miniaturizing EH devices to supply low power and small form factor IoT devices is still challenging and not always effective. In fact, EH from the miniaturized is more challenging due to strict constraints in terms of size, weight, and cost. Among other technologies, wireless power transmission is growing of popularity due to the fact that it can be controlled and it is less unpredictable than environments sources. This paper presents a review of technologies for wireless power transmission that can supply cm-scale low power IoT devices. Additionally, this paper provides in-field evaluation with mm-scale antennas of radio frequency power transmission using a commercial system: Powercast. Experimental results focus on evaluating the power flowing in the battery, which also includes the conversion losses, for different distances and antennas. Infield evaluation shows that it is possible to achieve mW of usable power to one meter and hundreds of µW at few meters. Those powers are enough for a wide range of low power and miniaturized IoT devices and IoT applications. In conclusion, this paper shows that RF transmission is an effective and alternative way to supply perpetually low power IoT devices.
Double-Domain Imaging and Adaption for Person Re-Identification

Person re-identification (re-ID) performance has been significantly boosted in recent works, but re-ID model trained on one dataset usually cannot work effectively on another one. To address this problem, we proposed a novel framework—double-domain translation generative adversarial network (DTGAN) that can train images between two domains and generalize the model trained on one domain well to another domain. We divide this paper into two steps. First, the source images are translated in an unsupervised manner, and the translated images retain the style of target images and the ID labels in the source domain. Second, the translated images are used as the training data for supervised feature learning. Besides, with the purpose of moderating the influence of noise, we employ the strategy of label smoothing regularization (LSR). In our experiments, we observe that the images generated by the DTGAN are of high quality and more appropriate for domain adaption. In addition, the re-ID accuracy of the DTGAN is competitive to the state-of-the-art methods on Market-1501 and DukeMTMC-reID.
Impact of Reduced Precision in the Reliability of Deep Neural Networks for Object Detection

Modern Graphics Processing Units (GPUs) have dedicated hardware to execute floating-point operations with different precisions (64-bit double, 32-bit single, and 16-bit half). Using reduced precision for specific applications like Deep Neural Networks (DNNs) has been shown to reduce both the execution time and power consumption with negligible effects on the DNNs' accuracy. As GPUs are playing a critical role in DNN for object detection and get into safety-critical environments, their reliability is becoming a growing concern. In this paper, we evaluate the reliability of a DNN implemented in three different precisions (half, single, and double) on NVIDIA mixed-precision GPUs. We evaluate not only the error rate of the applications but also the effects of the errors on the final detection. We perform extensive fault-injection campaign on the register file of NVIDIA mixed-precision GPUs. We found that reducing data and operation precision increases the probability for the fault to impact the DNN detection and classification. Then, we complement the fault injection study with beam experiments. We exposed YOLOv3 running on Tesla V100s to neutron beams and found that the use of half precision reduces the error rate of up to 2x. The smaller exposed area and improved performances brought by reduced precision is then likely to increase the DNN reliability.
Optimized Scheduling Model for Isolated Microgrid of Wind-Photovoltaic-Thermal-Energy Storage System With Demand Response

With the further opening of the power market, the diversification and complication of the energy structure in the isolated microgrid environment poses great challenges for the operation of the grid. In order to promote the local consumption problem of renewable energy sources, this paper proposes an optimal scheduling model for the isolated microgrid of the windphotovoltaic-thermal-energy storage with the demand response. Firstly, in order to cope with the uncertainty of the integration of a large number of renewable energy sources, the use of thermal power units and energy storage systems combined with complementary power generation to achieve safe and economic of renewable energy. Then, aiming at the minimum power generation cost of the microgrid, according to the information on the current output of renewable energy, the price-based demand response strategy guides the reasonable use of electricity. Finally, the particle swarm optimization algorithm with adaptive compression factor (PSO-ACF) is used to solve the model. Taking an isolated microgrid as an example, the simulation results have verified the validity of the model. The results show that using the price-based demand response to guide the load demand has certain effects on realizing the system load "peak-shaving and filling valley" and reducing power generation cost of whole microgrid.
Human Behavior Recognition Based On CNN

The technology of human behavior recognition is a cutting-edge topic which covers many areas-including computer vision, pattern recognition and artificial intelligence, and it have been widely used in areas such as commercial products, medical testing and military fields. According to the principle of Convolution Neural Networks (CNN), this paper presents a CNN human behavior recognition method based on small sample, which can identify a variety of human behaviors and human interaction behaviors respectively. The experimental result shows that the method can accurately identify the behavior of single-person and double-person interaction, and the recognition rate is above 92%, which proves the effectiveness of the method.