Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.

Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm

Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.

Security Architecture on the Trusting Internet of Things

By analyzing existed Internet of Things' system security vulnerabilities, a security architecture on trusting one is constructed. In the infrastructure, an off-line identity authentication based on the combined public key (CPK) mechanism is proposed, which solves the problems about a mass amount of authentications and the cross-domain authentication by integrating nodes' validity of identity authentication and uniqueness of identification. Moreover, the proposal of constructing nodes' authentic identification, valid authentication and credible communication connection at the application layer through the perception layer impels the formation of trust chain and relationship among perceptional nodes. Consequently, a trusting environment of the Internet of Things is built, by which a guidance of designing the trusted one would be provided.

Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)technologies.In this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use cases.Next,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration approach.We review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration approaches.We also highlight the need for intelligence in resources integration.Then,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various classes.Finally,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.

Evaluating the Topology Coverage of BGP Monitors

BGP monitors are currently the main data resource of AS-level topology measurement,and the integrity of measurement result is limited to the location of such BGP monitors.However,there is currently no work to conduct a comprehensive study of the range of measurement results for a single BGP monitor.In this paper,we take the first step to describe the observed topology of each BGP monitor.To that end,we first investigate the construction and theoretical up-limit of the measured topology of a BGP monitor based on the valley-free model,then we evaluate the individual parts of the measured topology by comparing such theoretical results with the actually observed data.We find that:1)for more than 90%of the monitors,the actually observed peer-peer links merely takes a small part of all theoretical visible links;2)increasing the BGP monitors in the same AS may improve the measurement result,but with limited improvement;and 3)deploying multiple BGP monitors in different ASs can significantly improve the measurement results,but non-local BGP monitors can hardly replace the local AS BGP monitors.We also propose a metric for monitor selection optimization,and prove its effectiveness with experiment evaluation.

Studies on Multi-Criteria Decision-Making-Based Healthcare Systems Using the Machine Learning

There is a lot of information in healthcare and medical records.However,it is challenging for humans to turn data into information and spot hidden patterns in today’s digitally based culture.Effective decision support technologies can help medical professionals find critical information concealed in voluminous data and support their clinical judgments and in different healthcare management activities.This paper presented an extensive literature survey for healthcare systems using machine learning based on multi-criteria decision-making.Various existing studies are considered for review,and a critical analysis is being done through the reviews study,which can help the researchers to explore other research areas to cater for the need of the field.

Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

Where Does AlphaGo Go: From Church-Turing Thesis to AlphaGo Thesis and Beyond

An investigation on the impact and significance of the Alpha Go vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the Alpha Go Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the Alpha Go program provide an engineering solution for tackling issues in complexity and intelligence. Specifically,the Alpha Go Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with Alpha Go-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of Alpha Go and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation.

A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts

Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remedy this issue,a large body of research has been performed in recent years and many new algorithms have been proposed.This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts.We start with a brief introduction to the basic concepts,followed by a summary of the benchmark test problems with irregular problems,an analysis of the causes of the irregularity,and real-world optimization problems with irregular Pareto fronts.Then,a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses.Finally,open challenges are pointed out and a few promising future directions are suggested.

Discrete Event System Framework for Fault Diagnosis with Measurement Inconsistency:Case Study of Rogue DHCP Attack

Fault detection and diagnosis(FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy(AR), Principal component analysis(PCA), Discrete event system(DES) model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system parameters.Due to various reasons like faults, communication errors etc.,sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc.,have mechanisms to handle measurement inconsistency, however,they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent(MI)parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system(MIDES) framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state(s) the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pumpvalve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol(DHCP) server attack by mapping the attack to a fault in the DES framework.

Global Synchronization of Stochastically Disturbed Memristive Neurodynamics via Discontinuous Control Laws

This paper presents the theoretical results on the master-slave(or driving-response) synchronization of two memristive neural networks in the presence of additive noise. First,a control law with a linear time-delay feedback term and a discontinuous feedback term is introduced. By utilizing the stability theory of stochastic differential equations, sufficient conditions are derived for ascertaining global synchronization in mean square using this control law. Second, an adaptive control law consisting of a linear feedback term and a discontinuous feedback term is designed to achieve global synchronization in mean square, and it does not need prior information of network parameters or random disturbances. Finally, simulation results are presented to substantiate the theoretical results.

Computation of an Emptiable Minimal Siphon in a Subclass of Petri Nets Using Mixed-Integer Programming

Deadlock resolution strategies based on siphon control are widely investigated.Their computational efficiency largely depends on siphon computation.Mixed-integer programming(MIP)can be utilized for the computation of an emptiable siphon in a Petri net(PN).Based on it,deadlock resolution strategies can be designed without requiring complete siphon enumeration that has exponential complexity.Due to this reason,various MIP methods are proposed for various subclasses of PNs.This work proposes an innovative MIP method to compute an emptiable minimal siphon(EMS)for a subclass of PNs named S^(4)PR.In particular,many particular structural characteristics of EMS in S4 PR are formalized as constraints,which greatly reduces the solution space.Experimental results show that the proposed MIP method has higher computational efficiency.Furthermore,the proposed method allows one to determine the liveness of an ordinary S^(4)PR.

Unsupervised Quick Reduct Algorithm Using Rough Set Theory

Feature selection (FS) is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. In this paper, we propose a new unsupervised quick reduct (QR) algorithm using rough set theory. The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool. The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.

An Improved User Authentication and Key Agreement Scheme Providing User Anonymity

When accessing remote services over public networks, a user authentication mechanism is required because these activities are executed in an insecure communication environment. Recently, Wang et al. proposed an authentication and key agreement scheme preserving the privacy of secret keys and providing user anonymity. Later, Chang et al. indicated that their scheme suffers from two security flaws. First, it cannot resist DoS (denial-of-service) attack because the indicators for the next session are not consistent. Second, the user password may be modified by a malicious attacker because no authentication mechanism is applied before the user password is updated. To eliminate the security flaws and preserve the advantages of Wang et al.'s scheme, we propose an improvement in this paper.

Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

Electricity theft is one of the major issues in developing countries which is affecting their economy badly.Especially with the introduction of emerging technologies,this issue became more complicated.Though many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)techniques.Here,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations.Three levels of taxonomy are presented to classify state-of-the-art ETD techniques.Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature.The challenges of different ETD techniques and their mitigation are suggested for future work.It is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft tracking.This can help in the prevention of energy theft,in the future,as well as for ETD.

Enhancing Design of Visual-Servo Delayed System

A robust adaptive predictor is proposed to solve the time-varying and delay control problem of an overhead crane system with a stereo-vision servo. The predictor is based on the use of a recurrent neural network(RNN) with tapped delays, and is used to supply the real-time signal of the swing angle. There are two types of discrete-time controllers under investigation, i.e., the proportional-integral-derivative(PID) controller and the sliding controller. Firstly, a design principle of the neural predictor is developed to guarantee the convergence of its swing angle estimation. Then, an improved version of the particle swarm optimization algorithm, the parallel particle swarm optimization(PPSO) method is used to optimize the control parameters of these two types of controllers. Finally, a homemade overhead crane system equipped with the Kinect sensor for the visual servo is used to verify the proposed scheme. Experimental results demonstrate the effectiveness of the approach, which also show the parameter convergence in the predictor.

Hybrid NOMA Based MIMO Offloading for Mobile Edge Computing in 6G Networks

Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks. In this paper, we aim to minimize the transmission delay in the MIMO-MEC in order to improve the spectral efficiency, energy efficiency, and data rate of MEC offloading. Dinkelbach transform and generalized singular value decomposition (GSVD) method are used to solve the delay minimization problem. Analytical results are provided to evaluate the performance of the proposed Hybrid-NOMA-MIMO-MEC system. Simulation results reveal that the H-NOMA-MIMO-MEC system can achieve better delay performance and lower energy consumption compared to OMA.

Construction of General (k, n) Probabilistic Visual Cryptography Scheme

Visual cryptography scheme (VCS) is a secure method that encrypts a secret image by subdividing it into shadow images. Due to the nature of encryption VCS is categorized into two types: the deterministic VCS (DVCS) and the probabilistic VCS (PVCS). For the DVCS, we use m (known as the pixel expansion) subpixels to represent a secret pixel. The PVCS uses only one subpixel to represent a secret pixel, while the quality of reconstructed image is degraded. A well-known construction of (k, n)-PVCS is obtained from the (k, n)-DVCS. In this paper, we show another construction of (k, n)-PVCS by extending the (k, k)-PVCS.

Control of Non-Deterministic Systems With μ-Calculus Specifications Using Quotienting

The supervisory control problem for discrete event system(DES) under control involves identifying the supervisor, if one exists, which, when synchronously composed with the DES,results in a system that conforms to the control specification. In this context, we consider a non-deterministic DES under complete observation and control specification expressed in action-based propositional μ-calculus. The key to our solution is the process of quotienting the control specification against the plan resulting in a new μ-calculus formula such that a model for the formula is the supervisor. Thus the task of control synthesis is reduced a problem of μ-calculus satisfiability. In contrast to the existing μ-calculus quotienting-based techniques that are developed in deterministic setting, our quotienting rules can handle nondeterminism in the plant models. Another distinguishing feature of our technique is that while existing techniques use a separate μ-calculus formula to describe the controllability constraint(that uncontrollable events of plants are never disabled by a supervisor), we absorb this constraint as part of quotienting which allows us to directly capture more general state-dependent controllability constraints. Finally, we develop a tableau-based technique for verifying satisfiability of quotiented formula and model generation. The runtime for the technique is exponential in terms of the size of the plan and the control specification. A better complexity result that is polynomial to plant size and exponential to specification size is obtained when the controllability property is state-independent. A prototype implementation in a tabled logic programming language as well as some experimental results are presented.

Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing

In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world.As a part of the IoT ecosystem,task assignment has become an important goal of the research community.Existing task assignment algorithms can be categorized as offline(performs better with datasets but struggles to achieve good real-life results)or online(works well with real-life input but is difficult to optimize regarding in-depth assignments).This paper proposes a Cross-regional Online Task(CROT)assignment problem based on the online assignment model.Given the CROT problem,an Online Task Assignment across Regions based on Prediction(OTARP)algorithm is proposed.OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments.The first stage uses historical data to make offline predictions,with a graph-driven method for offline bipartite graph matching.The second stage uses a bipartite graph to complete the online task assignment process.This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies.To encourage crowd workers to complete crowd tasks across regions,an incentive strategy is designed to encourage crowd workers’movement.To avoid the idle problem in the process of crowd worker movement,a drop-by-rider problem is used to help crowd workers accept more crowd tasks,optimize the number of assignments,and increase utility.Finally,through comparison experiments on real datasets,the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.