Binary Archimedes Optimization Algorithm for Computing Dominant Metric Dimension Problem

In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B.A resolving set is dominating if every vertex of G that does not belong to B is a neighbor to some vertices in B.The dominant metric dimension of G is the cardinality number of the minimum dominant resolving set.The dominant metric dimension is computed by a binary version of the Archimedes optimization algorithm(BAOA).The objects of BAOA are binary encoded and used to represent which one of the vertices of the graph belongs to the dominant resolving set.The feasibility is enforced by repairing objects such that an additional vertex generated from vertices of G is added to B and this repairing process is iterated until B becomes the dominant resolving set.This is the first attempt to determine the dominant metric dimension problem heuristically.The proposed BAOA is compared to binary whale optimization(BWOA)and binary particle optimization(BPSO)algorithms.Computational results confirm the superiority of the BAOA for computing the dominant metric dimension.

Joint Power and Duty-Cycle Design Using Alternating Optimization Algorithm under Energy Harvesting Architectures

In the emerging sixth generation(6G)communication network,energy harvesting(EH)is a promising technology to achieve the unlimited energy supply and hence makes the wireless communication systems self-sustainable in terms of energy.However,in practice,the efficiency of energy harvesting is often low due to the limited device capability.In this paper,we formulate three types of different EH architectures,i.e.,the harvest-use architecture,the harvest-store-use architecture,and the harvest-use-store architecture from the perspective of energy storage efficiency.We propose resource allocation schemes to jointly design the sensor power and duty-cycle via an alternating optimization algorithm under the above EH architectures,in both simultaneous and non-simultaneous harvesting and utilization models,aiming at achieving a higher throughput and energy efficiency.Non-ideal circuit power is also considered.Numerical results show that our proposed schemes under EH architectures outperform the existing classic continuous transmission schemes.

Secrecy Rate Analysis for Reconfigurable Intelligent Surface-Assisted MIMO Communications with Statistical CSI

In this paper,a reconfigurable intelligent surface(RIS)-assisted MIMO wireless secure communication system is considered,in which a base station(BS)equipped with multiple antennas exploits statistical channel state information to communicate with a legitimate multi-antenna user,in the presence of an eavesdropper,also equipped with multiple antennas.We firstly obtain an analytical expression of the ergodic secrecy rate based on the results of largedimensional random matrix theory.Then,a jointly alternating optimization algorithm with the method of Taylor series expansion and the projected gradient ascent method is proposed to design the transmit covariance matrix at the BS,as well as the diagonal phaseshifting matrix to maximize the ergodic secrecy rate.Simulations are conducted to demonstrate the accuracy of the derived analytical expressions,as well as the superior performance of our proposed algorithm.

ON ALTERNATIVE OPTIMAL SOLUTIONS TO QUASIMONOTONIC PROGRAMMING WITH LINEAR CONSTRAINTS

In this paper, the nonlinear programming problem with quasimonotonic （ both quasiconvex and quasiconcave ）objective function and linear constraints is considered. With the decomposition theorem of polyhedral sets, the structure of optimal solution set for the programming problem is depicted. Based on a simplified version of the convex simplex method, the uniqueness condition of optimal solution and the computational procedures to determine all optimal solutions are given, if the uniqueness condition is not satisfied. An illustrative example is also presented.

Coalitional Game Based Joint Beamforming and Power Control for Physical Layer Security Enhancement in Cognitive IoT Networks

In this paper,the physical layer se-cure transmission in multi-antenna multi-user cogni-tive internet-of-thing(IoT)network is investigated,where the coalitional game based joint beamform-ing and power control scheme is proposed to im-prove the achievable security of cognitive IoT de-vices.Specifically,the secondary network consisting of a muti-antenna secondary transmitter,multiple sec-ondary users(SUs),is allowed to access the licensed spectrum resource of primary user(PU)with underlay approach in the presence of an unauthorized eaves-dropper.Based on the Merge-Split-Rule,coalitional game is formulated among distributed secondary users with cooperative receive beamforming.Then,an alter-native optimization method is used to obtain the op-timized beamforming and power allocation schemes by applying the up-downlink duality.The simulation results demonstrate the effectiveness of our proposed scheme in improving the SU’s secrecy rate and system utility while guaranteeing PU’s interference thresh-old.

Fuzzy Clustering with Novel Separable Criterion

Fuzzy clustering has been used widely in pattern recognition, image processing, and data analysis An improved fuzzy clustering algorithm was developed based on the conventional fuzzy c-means （FCM） to obtain better quality clustering results. The update equations for the membership and the cluster center are derived from the alternating optimization algorithm. Two fuzzy scattering matrices in the objective function assure the compactness between data points and cluster centers, and also strengthen the separation between cluster centers in terms of a novel separable criterion. The clustering algorithm properties are shown to be an improvement over the FCM method＇s properties. Numerical simulations show that the clustering algorithm gives more accurate clustering results than the FCM method.

A Data-driven Method for Fast AC Optimal Power Flow Solutions via Deep Reinforcement Learning

With the increasing penetration of renewable energy,power grid operators are observing both fast and large fluctuations in power and voltage profiles on a daily basis.Fast and accurate control actions derived in real time are vital to ensure system security and economics.To this end,solving alternating current(AC)optimal power flow(OPF)with operational constraints remains an important yet challenging optimization problem for secure and economic operation of the power grid.This paper adopts a novel method to derive fast OPF solutions using state-of-the-art deep reinforcement learning(DRL)algorithm,which can greatly assist power grid operators in making rapid and effective decisions.The presented method adopts imitation learning to generate initial weights for the neural network(NN),and a proximal policy optimization algorithm to train and test stable and robust artificial intelligence(AI)agents.Training and testing procedures are conducted on the IEEE 14-bus and the Illinois 200-bus systems.The results show the effectiveness of the method with significant potential for assisting power grid operators in real-time operations.

Warhead power assessment based on double hierarchy hesitant fuzzy linguistic term sets theory and gained and lost dominance score method

Warhead power assessment of the anti-ship missile plays a vital role in determining the optimal design of missile, thus having important strategic research significance. However, in the assessment process, expert’s judgement will directly affect the assessment accuracy. In addition,there are many criteria involved in the missile design alternatives. Some criteria with poor performance may be compensated by other criteria with excellent performance, and then it is impossible to find the truly optimal alternative. Aimed at solving these problems, this paper proposes a synthetical assessment process based on fuzzy hesitant linguistic term set and the Gained and Lost Dominance Score(GLDS) method. In order to improve the assessment accuracy of experts and solve the problem that experts generate different opinions, combined with the advantages of fuzzy hesitant sets and linguistic term sets, the double hierarchy hesitant fuzzy linguistic term sets are used in this paper to improve the accuracy of expert’s judgement. In order to effectively combine expert’s experience with the data of criteria, the evidence theory and entropy weight method are used to transfer the expert’s judgement to the weight. In order to avoid selecting defective alternative of missile design, the GLDS is used to fuse expert information and criteria information. Sensitivity analysis shows that the assessment process has sensitivity to some extent. However, when the fluctuation of expert’s assessment makes the fluctuation of θ in the range of-5% to 5%, the impact on the results is not quite conspicuous. The analysis of calculation result and comparative analysis show that the assessment process proposed in this paper is accurate enough, has great advantage in selecting the current and potential optimal alternative of missile design, and avoids the alternatives with low criteria performance that cannot be compensated by other criteria being selected.

Deep Reinforcement Learning Based Real-time AC Optimal Power Flow Considering Uncertainties

Modern power systems are experiencing larger fluctuations and more uncertainties caused by increased penetration of renewable energy sources(RESs) and power electronics equipment. Therefore, fast and accurate corrective control actions in real time are needed to ensure the system security and economics. This paper presents a novel method to derive realtime alternating current(AC) optimal power flow(OPF) solutions considering the uncertainties including varying renewable energy and topology changes by using state-of-the-art deep reinforcement learning(DRL) algorithm, which can effectively assist grid operators in making rapid and effective real-time decisions. The presented DRL-based approach first adopts a supervised-learning method from deep learning to generate good initial weights for neural networks, and then the proximal policy optimization(PPO) algorithm is applied to train and test the artificial intelligence(AI) agents for stable and robust performance. An ancillary classifier is designed to identify the feasibility of the AC OPF problem. Case studies conducted on the Illinois 200-bus system with wind generation variation and N-1 topology changes validate the effectiveness of the proposed method and demonstrate its great potential in promoting sustainable energy integration into the power system.

Aerial intelligent reflecting surface for secure wireless networks:Secrecy capacity and optimal trajectory strategy

This work investigates the potential of the aerial intelligent reflecting surface(AIRS)in secure communication,where an intelligent reflecting surface(IRS)carried by an unmanned aerial vehicle(UAV)is utilized to help the communication between the ground nodes.Specifically,we formulate the joint design of the AIRS’s deployment and the phase shift to maximize the secrecy rate.To solve the non-convex objective,we develop an alternating optimization(AO)approach,where the phase shift optimization is solved by the Riemannian manifold optimization(RMO)method,while the deployment optimization is handled by the successive convex approximation(SCA)technique.Furthermore,to reduce the computational complexity of the RMO method,an element-wise block coordinate descent(EBCD)based method is employed.Simulation results verify the effect of AIRS in improving the communication security,as well as the importance of designing the deployment and phase shift properly.

High-voltage output triboelectric nanogenerator with DC/AC optimal combination method

The high-voltage power source is one of the important research directions of triboelectric nanogenerator(TENG).In this paper,a high-voltage output TENG(HVO-TENG)is proposed with direct current/alternating current(DC/AC)optimal combination method for wind energy harvesting.Through the optimal design of a direct current generation unit(DCGU)and an alternating current generation unit(ACGU),the HVO-TENG can produce DC voltage of 21.5 kV and AC voltage of 200 V,simultaneously.The HVOTENG can continuously illuminate more than 6,000 light emitting diodes(LEDs),which is enough to drive more possible applications of TENG.Besides,this paper explored application experiments on HVO-TENG.Demonstrative experiments indicate that the high-voltage DC output is used for producing ozone,while the AC output can light up ultraviolet(UV)LEDs.The HVOTENG can increase the ozone concentration(C)in an airtight container to 3 parts per million(ppm)after 7 h and continuously light up UV LEDs.All these demonstrations verify that the HVO-TENG has important guiding significance for designing high performance TENG.