Miracle-dz/UniFAR-FTUs huggingface dataset:
Indian Grid Code Based Low Voltage Ride Through Control Development for Large Scale Solar PCS

To meet with increasing demand of user's load, the integration of solar generation is promoted by various governments. As a mandatory requirement, the solar generation source should follow the grid code to maintain the grid stability as well as to enhance the system robustness by complying standards such as Low Voltage Ride Through (LVRT). In this paper, a control strategy for solar inverter is addressed with emphasising the LVRT operation based on reactive power injection. The proposed control strategy enables solar inverter to ride through grid voltage changes as per the Indian grid code without blocking of inverter's switching pulses. The fast response of current controllers along with feedforward control is adopted for cooperative operation of solar inverter during grid faults. The fast response of current controller is desired to shorten the reactive power recovery time, while the feedforward control is used to avoid current spike at the instance of grid fault. The proposed LVRT control strategy for solar inverter is validated by hardware test results of 1.25 MW solar inverter. The hardware test results confirm that the LVRT operation of solar inverter is in compliance with Indian grid code standard.
The AllScale API

Effectively implementing scientific algorithms in distributed memory parallel applications is a difficult task for domain scientists, as evident by the large number of domain-specific languages and libraries available today attempting to facilitate the process. However, they usually provide a closed set of parallel patterns and are not open for extension without vast modifications to the underlying system. In this work, we present the AllScale API, a programming interface for developing distributed memory parallel applications with the ease of shared memory programming models. The AllScale API is closed for modification but open for extension, allowing new, user-defined parallel patterns and data structures to be implemented based on existing core primitives and therefore fully supported in the AllScale framework. Focusing on high-level functionality directly offered to application developers, we present the design advantages of such an API design, detail some of its specifications and evaluate it using three real-world use cases. Our results show that AllScale decreases the complexity of implementing scientific applications for distributed memory while attaining comparable or higher performance compared to MPI reference implementations.
Dynamic Obstructed Group Trip Planning Queries in Spatial Databases

In this research work, we introduce dynamic obstructed group trip planning (DOGTP) query which allows a group of members to plan a trip dynamically with a minimum total travel distance in the obstructed space. Here, the group of members in a trip can join or leave or rejoin in the middle of a trip at any point of interests (POIs) i.e. restaurant, shopping mall, library or theater. Researchers developed solution for dynamic group trip planning queries in the Euclidean space. They did not consider obstacles in their problem space. In the obstructed space, members can face different types of obstacles i.e. building, private property, main road, lake etc. Therefore, the key challenges of our research work is to minimize the obstructed trip distance in order to find an optimal solution of the dynamic group trip planning queries in the obstructed space. Thus in this study, we extend the research work of dynamic group trip planing query in the Euclidean space into the Obstructed space. We develop solution for processing DOGTP query that returns the optimal trip for all group members with reduced I/O and query processing overhead.
Data Driven Method for Event Classification via Regional Segmentation of Power Systems

This paper presents a data-driven approach for event classification via a regional segmentation of power systems. The data-driven approach is suitable for the complex power systems under transient conditions, as it directly derives the information from the measurement signal database instead of modeling transient phenomena. However, measurement conditions of real-world power system will have unavoidable missing and bad data. Thus, it is necessary for data-driven model to have a robustness and adaptability about varying environment as well as system configurations and measurement conditions. In this work, the clustering-based regional segmentation of power systems is adopted to improve robustness of the data driven model by maintaining the fixed-input-feature format under varieties of measurement conditions. The clustering technique is applied to electrical buses for regional segmentation, and proposed features of phasor measurement unit (PMU) signals are extracted by integrating PMUs in each region based on wavelet analysis. As a result, the regional segmentation achieves improvement of data driven method for event classification with reduced number of calculations and management of bad data. Finally, we verify the event classification algorithm through a case study and analyze the performance of the algorithm for noise and computation time in addition to classification accuracy.
A Distributed Spatial Method for Modeling Maritime Routes

In this work we propose a novel spatial knowledge discovery pipeline capable of automatically unravelling the “roads of the sea” and maritime traffic patterns by analysing voluminous vessel tracking data, as collected through the Automatic Identification System (AIS). We present a computationally efficient and highly accurate solution, based on a MapReduce approach and unsupervised learning methods, capable of identifying the spatiotemporal dynamics of ship routes and most crucially their characteristics, thus deriving maritime “patterns of life” at a global scale, without the reliance on any additional information sources or a priori expert knowledge. Experimental results confirm high accuracy of results and superior performance in comparison to other methods, with the entire processing duration completing in less than 3 hours for more than a terabyte of non-uniform spatial data. Finally, to clearly demonstrate the applicability and impact of our proposed method, we evaluate its ability to detect real world “anomalies”, such as maritime incidents reported in the European Marine Casualty Information Platform. Numerical results show the advantages of our scheme in terms of accuracy, with an achieved anomaly detection accuracy of higher than 93%, by detecting 313 out of 335 relevant maritime incidents.
Autonomous Data-Race-Free GPU Testing

As the deep learning and high-performance computing markets continue to grow, hardware designers are increasingly optimizing future GPUs to run compute (a.k.a. GPGPU) workloads. A key area of optimization for these compute-oriented designs, which was not emphasized when GPUs exclusively executed graphics workloads, is inter-thread data sharing and synchronization. GPU cache coherence protocols now support these operations and are governed by a specified memory consistency model. In general, current GPU models are based on sequential consistency for data-race-free (SC for DRF), which mandates data written to memory must be globally visible only after certain synchronization points. GPU coherence protocols based on such relaxed memory models are particularly difficult to design and test due to the large number of memory accesses that may be reordered. This leaves GPU hardware designers struggling to validate the correctness of GPU cache coherence optimizations. To address this issue, this paper introduces a novel, completely autonomous random testing methodology for complex GPU cache coherence protocols. Our framework continuously generates sequences of memory requests with minimal user intervention using a mix of load, store, and atomic operations. The tester dynamically and autonomously checks each response against an expected global view of memory and immediately detects any inconsistencies in a target coherence protocol, providing designers detailed feedback on the issue. We then demonstrate the methodology on the popular cycle-level gem5 simulator by replacing its GPU core model with our unique testing framework. The results show that the GPU tester can cover 94% and 100% of all reachable state transitions in L1 and L2 caches respectively of a representative GPU coherence protocol. This coverage is 6.25% and 25% higher than the one achieved by a wide selection of 26 applications. In addition, the tester runs more than 50 times faster than those applications, which enables efficient and fast protocol debugging.
Leveraging SDN and NFV for Provisioning Quality Network Infrastructure for Filipino Public Schools

ICT in education plays a role in encouraging and improving the creativity and critical thinking of students. In the Philippines, public high schools suffer from slow and intermittent internet connection which can negatively affect the quality of education that the students get. With the help of Software Defined Networking and Network Function Virtualization, we propose a local caching system to alleviate the effects of the aforementioned problem. This solution is remotely configurable and automatically scaling which lessens the need for a network administrator to maintain the system. The local caching system can operate between two modes, Hot and Cold. The system's two modes are tested and compared with each other. Experimental results show that the Hot mode reacts 42 seconds faster to changes in the internet connection while the Cold mode reacts slower but uses fewer resources overall. The automatic scaling is also tested and the results show that our system uses half the number of cores compared to an overprovisioned setup when the load is low.
A Comparative Analysis of Different MPC Controllers for Load Frequency Control for Interconnected Power System

This paper presents the effect of robust distributed model predictive control (RDMPC) on load frequency control (LFC) for three-area multiple power systems and compared with other conventional model predictive controllers (MPC) such as centralized, decentralized, and distributed model predictive control (MPCs). Simulations are done to investigate the performance of the proposed controller in the presence of generation rate constraint (GRC). Results reveal that for a change in load, RDMPC gives a better dynamic performance of the power system by keeping the frequency, tie-line power and area control error (ACE) within the range. Hence, RDMPC shows robustness with good dynamic response of the system.
GAN-Based Blur Restoration for Finger Wrinkle Biometrics System

Existing methods for iris, fingerprint, and 3D face recognition in mobile devices have constraints in terms of price and size owing to their use of additional cameras, lighting, and sensors. Additionally, visible light, camera-based 2D face recognition, palm print recognition, touchless fingerprint recognition, and finger knuckle print recognition are difficult to be used in mobile devices due to limitations in recognition performance and user inconvenience. In response to these problems, studies have been conducted on finger wrinkle recognition in mobile devices; however, image quality is often reduced by motion blurring caused by the movement of the camera or the user’s finger, thereby reducing recognition performance. This study proposes a method for restoring and recognizing motion-blurred finger wrinkle images based on a generative adversarial network and deep convolutional neural network. Experiments were performed using two types of finger wrinkle databases, which were custom-made from images of 33 people captured by smart phone cameras (Dongguk mobile finger wrinkle database versions 1 and 2, denoted as DMFW-DB1 and DMFW-DB2, respectively). The results demonstrated high restoration and recognition performance in comparison with the state-of-the-art methods.
Diagnostics of the Cardiovascular System Based on Neural Networks

The paper presents the results of studies of the analysis of the main indicators for the diagnosis of heart disease - electrocardiogram (ECG) based on neural networks. A method for training a neural network to classify ECG signals based on various possible states is proposed. The neural network was trained to classify signals according to various possible states. At the same time, heart rate variability parameters are extracted from ECG signals and used as input functions for the neural network. The signal processing is described, consisting of frequency filtering of signals, extraction and calculation of signs. The structure of the neural network and obtaining parameters in the learning process is presented. At the end of testing, an assessment of the effectiveness of classification is determined. For this, a wide range of methods for analyzing ECG signals was investigated. The main feature is to run a neural network with an ECG signal or functions extracted from the input to get an assumption of a cardiovascular condition or potential disease as output. A trained neural network can be used to classify ECG signals and thereby help diagnose the disease and determine the correct symptoms or types of disease.
On-chip Digital Calibration of Low-Voltage Analog ICs in Nanotechnologies

This work deals with methods of on-chip digital calibration for analog integrated circuits in nanotechnologies. Research and evaluation of existing calibration methods was done at the beginning, followed by the implementation of digital calibration for a variable gain amplifier (VGA) in 130 nm CMOS technology, operating under 0.6 V supply voltage. The purpose of calibration is the cancellation of the VGA input offset voltage VIOFF. The whole design was verified using Monte Carlo analysis and partially also by experimental measurement on 10 packaged chip samples. A student team project was aimed to the automation of the chip measurement process. Measurements of calibrated VGA (cVGA) reveals the VIOFF variation between 13 $\mu V$ and 823 $\mu V$. The calibrated circuit reaches DC gain of 30 dB, which well corresponds with the nominal VGA design. In order to evaluate the calibration effectiveness, measurements include also the VGA samples without calibration, realized in the same technology. The obtained VIOFF reaches $\mu=403\mu V$ and $\sigma=3,45mV$ over 60 unpackaged dies. The paper also proposes ”ping-pong” technique to enable continuous operation of the amplifier.
Remote Sensing Image Aircraft Detection Based on Feature Fusion across Deep Learning Framework

The detection of remote sensing image aircraft targets based on deep learning has practical and important significance in the fields of military reconnaissance and disaster rescue. As a typical representative of the two mainstream detection algorithms, YOLOv3 and Faster_R_CNN have good detection effects on remote sensing image aircraft targets. However, for low quality remote sensing images, the two detection algorithms also have the phenomenons of omission and false detection. To deal with this, this paper proposes a target detection algorithm (YF_R_CNN) for „Separate training, joint detection‟, which realizes the cross-platform detection feature fusion of YOLOv3 based on darknet framework and Faster_R_CNN based on tensorflow framework, effectively alleviating the problems of existing algorithms. The experimental results show that the detection accuracy of YF_R_CNN algorithm reaches 94.8%, which is 3.7% and 3.1% higher than YOLOv3 and Faster_R_CNN respectively. The detection accuracy is obviously improved, and the algorithm has better flexibility and robustness.
Image Transmission Using LoRa Technology with Various Spreading Factors

With the development of technology, today's Internet of Things (IoT) is not only to read the value of the sensor to monitor the system. The demand for image monitoring is also important. In this paper, we use LoRa (Long Range) technology to transmit the images. LoRa technology is defined on Low Power Wide Area Networks technology (LPWAN) of the IoT. In general, LoRa supports the transmission of small data coming from the sensors. Since it has these features with low power, long distance, low cost and low data rate, transmitting images will spend more time for LoRa. If LoRa transmission can decrease the time to transmit pictures, it will produce a wide range of applications in IoT system. In this paper, we focus on transmitting the image simultaneously with both different SF (Spreading Factor) parameters and antennas for LoRa. In other words, using various SF as the multiplexing method may improve the image transmission time. Through experiments, this method does have less time than that with a single SF. The results show that effective LoRa image transmission is possible.
Integrated Autonomous Relative Navigation Method Based on Vision and IMU Data Fusion

An integrated autonomous relative navigation method based on vision and IMU data fusion was proposed in this paper, which can improve the position accuracy effectively and has strong adaptability to environmental changes. Firstly, IMU pre-integration formula based on Runge Kutta method was derived, which can improve the pre-integration position accuracy and reduce the accumulated error effectively. Secondly, an inverse depth estimation method based on the mixed probability model was proposed during the system initialization process, which can improve the accuracy of camera depth estimation and provide better initial conditions for back-end optimization. Thirdly, a sliding window filtering method based on the probability graph was proposed, which can avoid repeated calculations and improve the sliding window filtering efficiency. Forthly, combined with the advantages of the direct method and the feature point method, a mixed re-projection optimization method was proposed, which can expand the application scope of the method and improve the optimization accuracy effectively. Finally, in the closed-loop optimization, a closed-loop optimization method based on similar transformation is proposed to eliminate the accumulated error. In order to verify the environmental adaptability of the method and the impact of closed-loop detection on the relative navigation system, indoor and outdoor experiments were carried out with a hand-held camera and an IMU. EuRoC dataset was used in the experiments and the proposed method was compared with some classical methods. The experimental results showed that this method has high accuracy and robustness.
Ease Access to Climate Simulations for Researchers: IS-ENES Climate4Impact

Easier access to climate data is very important for the climate change impact research communities. Many aspects are important for those users, such as extensive guidance, transparent access to datasets, on-demand processing capabilities (notably for data reduction). To fulfill this objective, the climate4impact (http://climate4impact.eu/) web portal and services has been developed in the European Union funded IS-ENES projects, targeting climate change impact modellers, impact and adaptation consultants, as well as other experts using climate change data. It provides to users harmonized access to climate model data through tailored services. One of the main objectives of climate4impact is to provide standardized web services and tools that are reusable in other portals. These services include web processing services, web coverage services and web mapping services. Tailored portals can be targeted to specific communities and/or countries/regions while making use of those services. Recently, it became obvious that to fulfill users' needs regarding on-demand data processing and calculations, the climate4impact platform had to be able to use existing research and e-infrastructures in order to offer scalable and flexible services. This is especially true in the current context of a large increase in the data volumes of climate science datasets. To easily accommodate heterogeneous systems, a containerized and modular approach is envisioned. Finally, in the context of data processing delegation, a robust approach for metadata, provenance and lineage is required.
A Chinese Question Answering System based on GPT

The Chinese question-answering system needs to select the most appropriate answer from the answer library for user according to the given question on the natural language form. Previous question-answering systems required modeling for specific task characteristics and designing multiple modules. This paper first proposes to use the Generative Pre-trained Transformer (GPT) to implement the Chinese question-answering system. To optimize and improve the model, this Chinese model pays more attention to the contextual content and semantic characteristics, and we designed a new method to train this model. This model reduces the number of modules in the question-answering system. This paper evaluates the model on the Document-Based Chinese Question and Answer (DBQA) dataset and achieves a 2.5% improvement in MRR/MAP over the latest lattice convolutional neural networks (Lattice CNNs). (Abstract)
Training Function Stability in Anomaly Intrusion Detection based Deep Learning

Deep Learning is a promising model used in both supervised and unsupervised classification as well as in feature extraction. It is applied to solve various research problems. In the present paper, the problem of Anomaly Network Intrusion Detection is studied. A supervised classifier based on deep learning with a conventional architecture of neural networks (CNN) is adopted to classify normal and abnormal network users behavior in order to detect attacks. Unlike usual metrics, such as the correct classification rate, used to select appropriate classifiers, the stability of the learning algorithm is used to choose an appropriate Deep Learning model for this application. Since the synaptic weights and consequently the performance of a deep learning classifier depend crucially on the optimization algorithm (gradient descent, conjugate gradient, etc), the choice of an algorithm that lead to stable performance independently of its initialization is of a great importance. A set of experiments based on the NSL-KDD dataset is achieved to compare different learning techniques in terms of accuracy and stability.
Developing an Agriculture Internet of Things (IoT) Application Taking the Plants Humidity Preventive Maintenance (PM) as an Example

The advanced Information and communication technology (ICT) technology is the driving force behind this fast-growing evolution. With the flourishing development of the Internet of things (IoT), the IoT-related application is everywhere. IoT has brought transformations in various fields, including the industry. IoT technology has been applied extensively in industries, including agriculture, manufacturing. IoT can be described as the interactive framework where numerous sensor, equipment, device, platform is synchronized and connected by using the gateway, third-party technologies, and cloud computing in a machine-to-machine (M2M) communication. Recently, the application of cloud computing in the IoT has attracted considerable attention from various researchers and professionals. Cloud computing has evolved, and develop a diverse type of service models such as agriculture as a service (AaaS) in the agriculture industry. AaaS is one of the cloud services series. This study surveyed agriculture IoT related studies from 2015 to 2019. It investigates the disciplinary distribution of emerging related topics. The findings of this study show that data, IoT, and are the key issues driving researches, which are associated with agriculture IoT. This study developed a remote control system in agriculture IoT. The results demonstrated that the proposed system offers the appropriate tool for the users to remote control equipment and build the preventive maintenance strategy.