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A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

Building Simulation 2024年第3期17卷 441-457页

作者： Mohammad Kaosain Akbar Manar Amayri Nizar Bouguila Concordia Institute for information Systems engineering Concordia UniversityMontrealCanada

Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compared

关键词： semi-supervised learning non-intrusive load monitoring middle-point thresholding deep learning TCN LSTM

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engineering the spectra of photon triplets generated from micro/nanofiber

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Chinese Physics B 2024年第3期33卷 408-414页

作者：瞿川郭东琴李笑笑刘振旗赵义张胜海卫正统 The College of Basic Department Information Engineering UniversityZhengzhou 450000China

Quantum light sources are the core resources for photonics-based quantum information processing.We investigate the spectral engineering of photon triplets generated by third-order spontaneous parametric down-conversion in micro/nanofiber.The phase mismatching at one-third pump frequency gives rise to non-degenerate photon triplets,the joint spectral intensity of which has an elliptical locus with a fixed eccentricity of√6/3.Therefore,we propose a frequency-division scheme to separate non-degenerate photon triplets into three channels with high heralding efficiency for the first time.Choosing an appropriate pump wavelength can compensate for the fabrication errors of micro/nanofiber and also generate narrowband,non-degenerate photon triplet sources with a high signal-to-noise ratio.Furthermore,the long-period micro/nanofiber grating introduces a new controllable degree of freedom to tailor phase matching,resulting from the periodic oscillation of dispersion.In this scheme,the wavelength of photon triplets can be flexibly tuned using quasi-phase matching.We study the generation of photon triplets from this novel perspective of spectrum engineering,and we believe that this work will accelerate the practical implementation of photon triplets in quantum information processing.

关键词： photon triplets micro/nanofiber spectrum engineering

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A whole-net cooperative positioning method based on CDGNSS and inter-vehicle ranging

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Chinese Journal of Aeronautics 2024年第2期37卷 401-416页

作者： Shan HU Hongbo ZHAO Chen ZHUANG School of Electronics and information engineering Beihang UniversityBeijing 100191China

Relative positioning is recognized as an important issue for vehicles in urban environments.Multi-vehicle Cooperative Positioning(CP)techniques which fuse the Global Navigation Satellite System(GNSS)and inter-vehicle ranging have attracted attention in improving the performance of baseline estimation between vehicles.However,current CP methods estimate the baselines separately and ignore the interactions among the positioning information of different baselines.These interactions are called’information coupling’.In this work,we propose a new multivehicle precise CP framework using the coupled information in the network based on the Carrier Differential GNSS(CDGNSS)and inter-vehicle ranging.We demonstrate the benefit of the coupled information by deriving the Cramer-Rao Lower Bound(CRLB)of the float estimation in CP.To fully use this coupled information,we propose a Whole-Net CP(WN-CP)method which consists of the Whole-Net Extended Kalman Filter(WN-EKF)as the float estimation filter,and the Partial Baseline Fixing(PBF)as the ambiguity resolution part.The WN-EKF fuses the measurements of all baselines simultaneously to improve the performance of float estimation,and the PBF strategy fixes the ambiguities of the one baseline to be estimated,instead of full ambiguity resolution,to reduce the computation load of ambiguity resolution.Field tests involving four vehicles were conducted in urban environments.The results show that the proposed WN-CP method can achieve better performance and meanwhile maintain a low computation load compared to the existing methods.

关键词： GNSS Cooperative positioning Baseline Ambiguity resolution information coupling

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Modulated symbol-based one-time pad secure transmission scheme using physical layer keys

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Science China(information Sciences) 2024年第1期67卷 221-239页

作者： Xiaoyan HU Zheng WAN Kaizhi HUANG Liang JIN Mengyao YAN Yajun CHEN Jinmei YANG information engineering university Purple Mountain Laboratories for Network and Communication Security

This paper proposes a novel modulated symbols-based one-time pad(SOTP) secure transmission scheme using physical layer keys.Unlike classical physical layer key generation and exclusive OR(XOR)encryption in the discrete binary space,we design a framework for modulated symbols-based one-time pad(OTP) encryption,where the cryptographic primitive and mathematical model of SOTP is given to build a practical cryptographic protocol.Compared with existing physical layer encryption(PLE) schemes,we provide rigorous proof that the framework can meet perfect secrecy and correctness requirements.In addition,we provide a specific scheme of physical layer OTP secure transmission for quadrature amplitude modulation(QAM) and phase-shift keying(PSK) symbols based on physical layer keys.This scheme realizes the unification of bit encryption and symbol encryption,which can adaptively select the quantization level according to the signal-to-noise ratio(SNR) to minimize the symbol error rate(SER).Further,we analyze the performance quantitatively and derive the closed-form expressions of SER,which indicates that the proposed scheme has a lower SER.Finally,simulation results verify that the proposed symbol-wise OTP secure transmission scheme can achieve perfect secrecy and high reliability.

关键词： physical layer security one-time pad wireless channel physical layer encryption perfect secrecy

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Product quality prediction based on RBF optimized by firefly algorithm

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Journal of Systems engineering and Electronics 2024年第1期35卷 105-117页

作者： HAN Huihui WANG Jian CHEN Sen YAN Manting College of Electronics and information engineering Tongji UniversityShanghai 201804China

With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

关键词： product quality prediction data pre-processing radial basis function swarm intelligence optimization algorithm

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Advancements in the modification of magnesium-based hydrogen storage materials

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Progress in Natural Science:Materials International 2024年第3期34卷 540-554页

作者： Lin Yang Wen Zeng Yanqiong Li College of Materials Science and engineering Chongqing University School of Electronic information & Electrical engineering Chongqing University of Arts and Sciences

Magnesium-based hydrogen storage materials represent a hydrogen storage technology with broad application prospects. As the global energy crisis and environmental pollution issues become increasingly severe, hydrogen,as a clean and efficient energy source, has garnered growing attention. Magnesium-based hydrogen storage,serving as a crucial means for storing and transporting hydrogen, is gaining prominence due to its abundant resources, low cost, low density, and high hydrogen storage density. However, challenges in terms of absorption/desorption rates, temperature, activation energy, and enthalpy during hydrogen application impede its development. To address these challenges, this paper systematically reviews current research on magnesium-based hydrogen storage materials, encompasses their types, characteristics, and hydrogen absorption mechanisms.Furthermore, it delves into the impacts of nanoscale dimensions, alloying, doping, and catalysis on the performance of magnesium-based materials. The aim is to provide valuable insights for research in related fields.

关键词： For hydrogen storage Magnesium-based materials Research on modifying

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A Review of NILM Applications with Machine Learning Approaches

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Computers, Materials & Continua 2024年第5期79卷 2971-2989页

作者： Maheesha Dhashantha Silva Qi Liu School of Electronic and information engineering Nanjing University of Information Science and TechnologyNanjing210044China

In recent years,Non-Intrusive LoadMonitoring (NILM) has become an emerging approach that provides affordableenergy management solutions using aggregated load obtained from a single smart meter in the power grid.Furthermore, by integrating Machine Learning (ML), NILM can efficiently use electrical energy and offer less ofa burden for the energy monitoring process. However, conducted research works have limitations for real-timeimplementation due to the practical issues. This paper aims to identify the contribution of ML approaches todeveloping a reliable Energy Management (EM) solution with NILM. Firstly, phases of the NILM are discussed,along with the research works that have been conducted in the domain. Secondly, the contribution of machinelearning approaches in three aspects is discussed: Supervised learning, unsupervised learning, and hybridmodeling.It highlights the limitations in the applicability of ML approaches in the field. Then, the challenges in the realtimeimplementation are concerned with six use cases: Difficulty in recognizing multiple loads at a given time,cost of running the NILM system, lack of universal framework for appliance detection, anomaly detection andnew appliance identification, and complexity of the electricity loads and real-time demand side management.Furthermore, options for selecting an approach for an efficientNILMframework are suggested. Finally, suggestionsare provided for future research directions.

关键词： Non-intrusive load monitoring transfer learning machine learning feature extraction

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Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

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China Communications 2024年第5期21卷 113-122页

作者： Qiu Wanqing Zhang Qingmiao Zhao Junhui Yang Lihua School of information engineering East China Jiaotong UniversityNanchang 330013China School of Electronic and information engineering Beijing Jiaotong UniversityBeijing 100044China

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

关键词： extreme learning machine fingerprinting localization indoor localization machine learning particle swarm optimization

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Deployment of Edge Computing Nodes in IoT:Effective Implementation of Simulated Annealing Method Based on User Location

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China Communications 2024年第1期21卷 279-296页

作者： Junhui Zhao Ziyang Zhang Zhenghao Yi Xiaoting Ma Qingmiao Zhang School of Electronic and information engineering Beijing Jiaotong UniversityBeijing 100044China School of information engineering East China Jiaotong UniversityNanchang 330013China

Edge computing paradigm for 5G architecture has been considered as one of the most effective ways to realize low latency and highly reliable communication,which brings computing tasks and network resources to the edge of network.The deployment of edge computing nodes is a key factor affecting the service performance of edge computing systems.In this paper,we propose a method for deploying edge computing nodes based on user location.Through the combination of Simulation of Urban Mobility(SUMO)and Network Simulator-3(NS-3),a simulation platform is built to generate data of hotspot areas in Io T scenario.By effectively using the data generated by the communication between users in Io T scenario,the location area of the user terminal can be obtained.On this basis,the deployment problem is expressed as a mixed integer linear problem,which can be solved by Simulated Annealing(SA)method.The analysis of the results shows that,compared with the traditional method,the proposed method has faster convergence speed and better performance.

关键词： deployment problem edge computing internet of things machine learning

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Flight Time Minimization of UAV for Cooperative Data Collection in Probabilistic LoS Channel

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China Communications 2024年第2期21卷 210-226页

作者： Yan Li Shaoyi Xu Yunpu Wu Dongji Li School of Electronic and information engineering Beijing Jiaotong UniversityBeijing 100044China School of Electrical engineering and Electronic information Xihua UniversityChengdu 610039China

This paper investigates the data collection in an unmanned aerial vehicle(UAV)-aided Internet of Things(IoT) network, where a UAV is dispatched to collect data from ground sensors in a practical and accurate probabilistic line-of-sight(LoS) channel. Especially, access points(APs) are introduced to collect data from some sensors in the unlicensed band to improve data collection efficiency. We formulate a mixed-integer non-convex optimization problem to minimize the UAV flight time by jointly designing the UAV 3D trajectory and sensors’ scheduling, while ensuring the required amount of data can be collected under the limited UAV energy. To solve this nonconvex problem, we recast the objective problem into a tractable form. Then, the problem is further divided into several sub-problems to solve iteratively, and the successive convex approximation(SCA) scheme is applied to solve each non-convex subproblem. Finally,the bisection search is adopted to speed up the searching for the minimum UAV flight time. Simulation results verify that the UAV flight time can be shortened by the proposed method effectively.

关键词： data collection flight time probabilistic line-of-sight channel unlicensed band unmanned aerial vehicle

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