Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Customer Connectivity to Distribution Transformer via Application of Machine Learning Techniques to Smart-Meter Data

Accurate topology information is essential for the efficient operation of a power distribution network. However, due to the constant changes in the physical topology, maintaining this accurate information is a challenging task. This research attempts to solve a common predicament faced by distribution network service providers (DNSPs), by providing an accurate connectivity model of the power distribution network. This would provide an accurate map as to which customer is powered by which distribution transformer. The dataset used for this study includes the average RMS voltage values from 710 smart meters powered by five transformers, every 15 minutes for five months. A classification model that can predict the smart meter connectivity to its respective transformer with an average accuracy of 90% is obtained. Initially, similarity values are calculated using the Euclidean distance measure by comparing the voltage profiles of each smart meter with that of a selected reference meter from each transformer. Next, this data is used to train the classifier using the algorithm created. Finally, an unknown set of smart meter data is tested using the trained model to observe the prediction accuracy. The solution provided could be further developed by adding features such as phase identification.
LFM Interference Suppression Algorithm Based on FrFT

Differential frequency hopping (DFH) communication system is widely used in the field of confidential communication with strong anti-jamming ability. Linear frequency modulation (LFM) signal with a broadband non-stationary characteristic can cause a great influence on the DFH communication system. In order to solve the problem of anti LFM interference in DFH system, this paper proposal a LFM interference suppression algorithm which is based on FrFT(Fractional Fourier Transform). By combining different order in FrFT, the optimal order is extracted and the LFM interference signal is identified. Further LFM interference suppression is realized. The simulation results show that the algorithm can effectively mitigate LFM interference in DFH communication system to improve the SNR.
Research on Area Spectrum Efficiency and Energy Efficiency in 3-tier Ultra-Dense Heterogeneous Cellular Networks

Cellular network is usually modeled and analyzed based on two-dimensional space. However, the two-dimensional space model is only appropriate for the scene where base stations are distributed sparsely and has single access node. We aim at dense district with a great of low-power base stations, basing on the theory of stochastic geometry, propose a downlink 3-tier ultra-dense heterogeneous network model in three-dimensional (3D) space. The influence of density of base station on network performance is emphatically analyzed. Firstly, on the basis of this model, the probability density function of the distance between the target user and the base station in 3D space is given. Afterwards, the coverage probability of each layer and the average accessible rate of user in different layers are derived, area spectrum efficiency (ASE) and energy efficiency (EE) are derived at the same time, and the impact of each tier base station density on ASE and EE is analysed. Subsequently, we propose a simple optimization problem. That is when the ASE meets certain constraints, the EE can be maximized by optimizing the density of base station, then they are balanced. It is shown that the density of each tier base station and the density ratio between tiers have huge impact on system performance, and there are thresholds to make the deployment optimal. On the condition of dense environment, the analysis of 3D space heterogeneous network is more precise and practical. We provide more perfect theoretical support for base station deployment and green communication in the next generation mobile communication network.
A High-performance IoT Middleware Based on Reinforcement Learning

This paper proposes a high-performance Internet of Things (IoT) middleware based on reinforcement learning. The adaptive decision-maker is realized by the K-armed bandit algorithm, which can dynamically resize the server accept queue. This paper adopts service-oriented architecture(SOA) to make the middleware architecture be loosely-coupled, reusable, and easily extensible. Through standardized service interfaces, users can transparently interact with heterogeneous IoT devices. In addition, the high availability of the middleware platform is achieved through non-blocking IO, caching, and message queuing technologies. Through the experiments over Apache JMeter, the results verify that our IoT middleware has a better performance under high concurrency scenarios.
Space-Frequency-Interpolated Radio Map

This paper presents a novel method for radio map construction that simultaneously interpolates the received signal power values over space and frequency domains. Radio maps can be used to improve spectrum management and for localization systems, which are related to wireless systems in general such as cellular systems, Internet of Things (IoT) networks, and vehicular communications. Researchers have shown that crowdsourcing using spatial interpolation techniques can be used to accurately construct a radio map to improve these applications; however, because of the limitation in the spatial domain, conventional methods can build a radio map for only those frequencies at which sensing can be performed. Our proposed method focuses on the fact that the shadowing values show strong correlation over a wide range of frequency domains. The main idea is to treat the shadowing values obtained over various frequencies as the ones over target frequency. Using the actual datasets obtained over 870, 2115, and 3500 MHz in a cellular system, we show that the proposed method can accurately generate a radio map, even if no (or few) datasets are available in the target frequency.
Comparative Study of Markov Chain With Recurrent Neural Network for Short Term Velocity Prediction Implemented on an Embedded System

Short-term prediction models for an ego-vehicle’s speed contributes to the improvement of vehicle safety, driveability, and fuel economy. To achieve these desired outcomes, an accurate forward speed prediction model and its successful implementation in a real system is a prerequisite. This paper compares six velocity prediction models based on two types of data-driven models, a Markov chain and a Recurrent Neural Network (RNN), by implementing them in an embedded system to evaluate their prediction accuracy and execution time. The inputs to each model are the driving information acquired on a specific route, such as internal vehicle information, relative speed and distance to the vehicle in the front of the ego-vehicle, and ego-vehicle’s location estimated by the GPS signal along with the B-spline roadway model. The proposed prediction models predict the velocity profile of the ego-vehicle up to the prediction horizon of 150 m. The parameters of the proposed models have been optimized using Hyper-parameter Optimization via Radial basis function and Dynamic coordinate search. By applying real driving data, the Markov chain-based models show slightly lower prediction accuracy but shorter execution time than those of the RNN-based models.
Leveraging Machine Learning to Explain the Nature of Written Genres

The analysis of discourse and the study of what characterizes it in terms of communicative objectives is essential to most tasks of Natural Language Processing. Consequently, research on textual genres as expressions of such objectives presents an opportunity to enhance both automatic techniques and resources. To conduct an investigation of this kind, it is necessary to have a good understanding of what defines and distinguishes each textual genre. This research presents a data-driven approach to discover and analyze patterns in several textual genres with the aim of identifying and quantifying the differences between them, considering how language is employed and meaning expressed in each particular case. To identify and analyze patterns within genres, a set of linguistic features is first defined, extracted and computed by using several Natural Language Processing tools. Specifically, the analysis is performed over a corpora of documents—containing news, tales and reviews—gathered from different sources to ensure an heterogeneous representation. Once the feature dataset has been generated, machine learning techniques are used to ascertain how and to what extent each of the features should be present in a document depending on its genre. The results show that the set of features defined is relevant for characterizing the different genres. Furthermore, the findings allow us to perform a qualitative analysis of such features, so that their usefulness and suitability is corroborated. The results of the research can benefit natural language discourse processing tasks, which are useful both for understanding and generating language.
Efficient Confidentiality for Network Coded Distributed Storage

Coding techniques have improved the performance of distributed cloud systems (DCS). On one hand, they increased the reliability while trading off storage and bandwidth costs, and on the other hand, they provide high download speeds of data without centralized protocols. The higher data rates and lower delays are achieved by increasing the redundancy stored in clouds with higher bandwidth, allowing them to send more information before running dry. However, storing much information in a small set of clouds can violate confidentiality of the data in the presence of an attacker that breaks into these clouds. In this paper, we propose a technique that uses light-weight secure network coding schemes to increase the download rates of DCS. We compare the secure network coding techniques in terms of delay and codecs throughput, and we show that in multi-cloud systems, it is possible to obtain gains of 379% over DCS with equally shared redundancy and 192% over SotA techniques that aim to maximize the data rates by distributing more redundancy to the clouds with highest bandwidth.
GPS Spoofing Detector with Adaptive Trustable Residence Area for Cellular based-UAVs

The envisioned key role of Unmanned Aerial Vehicles (UAVs) in assisting the upcoming mobile networks calls for addressing the challenge of their secure and safe integration in the airspace. The GPS spoofing is a prominent security threat of UAVs. In this paper, we propose a 5G-assisted UAV position monitoring and anti-GPS spoofing system that allows live detection of GPS spoofing by leveraging Uplink received signal strength (RSS) measurements to cross-check the position validity. We introduce the Adaptive Trustable Residence Area (ATRA); a novel strategy to determine the trust area within which the UAV's GPS position should be located in order to be considered as non-spoofed. The performance evaluation shows that the proposed solution can successfully detect spoofed GPS positions with a rate of above 95%.
A Streaming-based Algorithm for Overlapping Community Detection

Mining the overlapping community structure is a very important task in complex network analysis. With the development of social network and Internet of things, the scale of data is becoming larger and larger. Many existing algorithms can not deal with such a large-scale network. In order to improve the efficiency of community detection in large-scale networks, an overlapping community detection algorithm based on graph streaming is proposed. The algorithm processes the edges in the network in a streaming mode, processes only one edge at a time and each edge is processed only once and then discarded. The algorithm divides the nodes according to the degree of node, the contribution of node to the community, and the number of connected edges between communities before and after the node's moving. Experimental results on real-life networks show that our proposed approach can quickly discover overlapping community structures in large-scale networks with linear computational complexity.
InceptionSSVEP: A Multi-Scale Convolutional Neural Network for Steady-State Visual Evoked Potential Classification

This paper presents a deep learningbased classification model, referred to as InceptionSSVEP, for the steady-state visual evoked potential (SSVEP) based braincomputer interface (BCI). InceptionSSVEP adopts the main concept of Inception network, which is a deep learning model performing well in image classification tasks, to improve the performance of SSVEP classification. A multi-scale convolution structure is utilized in InceptionSSVEP to extract both long-term and short-term features of SSVEP signals, for the purpose of ensuring the comprehensiveness of high-dimensional features in extracted SSVEPs. Moreover, a data enhancement scheme is proposed to overcome the impact of SSVEP data amount limitation on classifier training. Results show that the proposed InceptionSSVEP outperforms other existing methods significantly, and validate that Inception networks have good transferability on SSVEP signals. Reasons for the good performance of InceptionSSVEP are analyzed using deep learning interpretability tools.
A Novel Weighted Integration Dynamic Time Regularization and Euclidean Distance Optimization Algorithm for Power Data Mining

With the development of large-scale construction of smart grid, the edge terminal equipment of power grid will produce a large number of time series power data with great redundancy, which brings great challenges to the storage of edge side of the equipment. In order to reduce the storage cost of edge side, data mining and weight removal are needed. The traditional data mining technology generally adopts the data mining method based on dynamic time regularity, but the disadvantage is that the mining efficiency is low and the adjacent data with low similarity can not be weighed. Aiming at these problems, this paper proposes an algorithm based on weighted integration dynamic time-regulation and Euclidean distance optimization, which can eliminate data redundancy, achieve data mining and weight removal by calculating the similarity between data. Finally, based on the real sampling data of smart grid, the effect of the proposed data mining technology in edge computing security protection system is analyzed and verified.
On the Sum-Rate of Contention Resolution in Massive MIMO With NOMA

In this paper, we investigate the random access (RA) problem in a massive MIMO scenario. To deal with the continuous increase in the number of users, and the difficulty associated with sharing pilots among such users in order to organize channel access, a strongest-user collision resolution (SUCRe) protocol is modified to include a non-orthogonal multiple access (NOMA) approach. The proposed NOMA-RA protocol allows to resolve collisions between users who try to access the medium using the same pilot signal. Numerical results show that the proposed NOMA-RA strategy achieves a better performance in terms of sum-rate with lower average delay in comparison with the traditional SUCRe scheme, even considering imperfect channel estimation and successive interference cancellation. For instance, in a scenario when 10 pilot sequences are available and 20 devices compete to access the channel, comparing with the SUCRe protocol, the average delay is reduced by 30% while the sum-rate is improved by 33% and 22% considering, respectively, perfect and imperfect SIC decoders. In addition, the percentage of users that fail to access the BS in this situation drops from 66.5% with SUCRe to 38.5% with the proposed NOMA-RA scheme.
An Effective Controller Placement Algorithm Based on Clustering in SDN

Different from the traditional TCP/IP network, Software Defined Network (SDN) uses a layered architecture to decouple the control plane and data plane. The controller is the core device of the control plane, and the control plane is mainly responsible for the decision-making control of the routing strategy. Because the processing power of a single controller is limited, the multi-controller network architecture has become an inevitable choice in the WAN. The layout of the controllers in SDN has an impact on the performance of the entire network. The controller placement problem (CPP) is an NP-hard problem. The challenges of CPP solution are as follows. 1) the number of controllers; 2) the position of the controllers and the position of the switches under their management in the network topology. The clustering by fast search and find of density peaks (CFSFDP) algorithm is a density-based clustering algorithm. It has few initial parameters and does not require iterative solution. However, the important parameter $d_{c}$ of the CFSFDP algorithm is usually selected based on experience, and the number of cluster centers cannot be determined automatically. This paper proposes a new controller placement algorithm by improving the CFSFDP algorithm. We use the idea of information entropy to automatically determine the $d_{c}$ value, and automatically determine the number and location of the controllers based on the jumping point in the decision graph. It can be seen from the experimental results that compared with random placement, k-means and CFSFDP algorithm, our proposed algorithm can effectively reduce network propagation delay and improve network performance.
A Method for Constructing Heterogeneous Entities Pool in NFV Security Architecture Based on Mimic Defense

The characteristics of resource sharing and centralized deployment of network function virtualization (NFV) make the physical boundary under the traditional closed management mode disappear, bringing many new security threats to the network. To improve the security of the NFV network, this paper proposes a network function virtualization security architecture based on mimic defense. At the same time, to ensure the differences between heterogeneous entities, a genetic algorithm-based heterogeneous entities pool construction method is proposed. Simulation results show that this method can effectively guarantee the difference between heterogeneous entities and increase the difficulty of attackers.
Vehicle Auxiliary Driving System Based on Image Processing

Traffic signs are road facilities that use words or symbols to guide, restrict, warn or indicate information. Traffic signs are characterized by safety, striking setting, clear and bright. Setting traffic signs is an important measure to implement traffic management and ensure road traffic safety and smoothness. Drivers can know the road condition in front of them through traffic signs, so as to make adjustments. However, drivers often neglect traffic signs and misjudge traffic signs information because of their tired spirit, spiritual fluctuation, answering mobile phones during driving and bad weather, which will lead to serious traffic safety accidents. The purpose of the driver assistance system based on image recognition is to alert drivers through voice broadcasting and interface to avoid serious traffic accidents caused by ignoring traffic signs. After the driver opens the system, the system captures the road scene in front of him through the camera, and detects every frame of the image to determine whether there are traffic signs and what the content of the traffic signs are, and then alerts the driver through voice and image.
Sparse Satellite Constellation Design for LoRa-based Direct-to-Satellite Internet of Things

A global Internet of Things is possible by embracing constellations of satellites acting as orbiting gateways in a Direct-to-Satellite IoT (DtS-IoT). By removing the dependency on ground gateways, DtS-IoT enables a direct service on the regions illuminated by the passing-by satellite. After an in-depth overview of relevant experiments and candidate technologies, we discover that specific configurations of the Long-Range (LoRa) network protocol specification are particularly appealing to realize the DtS-IoT vision. Specifically, we profit from the maximum clock drift permitted on LoRa devices to propose the sparse satellite constellations concept. This approach significantly reduces the in-orbit DtS-IoT infrastructure at the expense of latency anyway present in resource-constrained IoT networks. We then introduce a novel algorithm comprising specific heuristics to design quasi-optimal topologies for sparse IoT constellations. Obtained results show that LoRa-compatible DtS-IoT services can already be provided world-wide with 10% and 4% of the satellites required for a traditional dense constellation, in different configurations.
A Hybrid Modular Cascade Machines System for Electric Vehicles Using Induction Machine and Permanent Magnet Synchronous Machine

Modular Cascade Machines (MCM) system is a multi-machine system for the traction systems of electrified vehicles. Currently, MCM is composed of several machines of the same type, generally, several Permanent Magnet Synchronous Machines (PMSM). This paper proposes a hybrid MCM system, which is composed of a PMSM and an Induction Machine (IM). This combination aims to fully benefit the properties of different machines. This hybrid MCM is compared with other traction systems, namely, PMSM-based MCM and single PMSM drive. For a fair comparison, the design specifications of all the traction systems are optimally sized by a specific sizing method, which can maximize the system high-efficiency area. Then, different traction systems are designed by Finite Element Method (FEM), in order to have accurate material consumptions and efficiency maps. The comparison study shows that the proposed hybrid MCM is competitive for energy saving. Therefore this new drive saves about 5% of energy in comparison with a single PMSM derive. The hybrid MCM energy saving is very close to the one of a PMSM-based MCM. Moreover, the hybrid MCM system reduces the PM volume (reduced by 53%) while increases the usage amount of winding copper, steel and aluminum, due to the existence of IM. Fortunately, the cost estimation proves that the hybrid MCM (not include inverters) has almost the same cost as the single PMSM. Hence the proposed hybrid MCM is a competitive alternative of the single PMSM than the PMSM-based MCM.
Energy-Efficiency Optimization of UAV-Assisted Internet of Things

In recent years, the use of unmanned aerial vehicles (UAVs) as aerial base stations to assist internet of things (IoT) has attracted wide interest from academia and industries due to UAV's high mobility, flexibility and low cost. To overcome the shortcomings of data backhaul in IoT, a UAV-assisted IoT framework is proposed in this paper. To maintain lifetimes of internet of things devices (IoTDs), the energy-efficiency of UAV-assisted IoT is maximized by jointly optimizing IoTDs' wake-up scheme and the fixed-wing UAV's trajectory. Since the optimization problem of energy-efficiency of UAV-assisted IoT is formulated as a mixed integer non-convex problem, an iterative algorithm is proposed by adopting successive convex approximation techniques. Simulation results show that the proposed algorithm achieves a higher energy-efficiency in UAV-assisted IoT compared with the traditional benchmark algorithm.