Results in Control and Optimization最新文献

In this paper, we introduce a new class of generalized $(h,\phi )$-G-type I vector-valued functions, by combining the notions of $(h,\phi )$-differentiable functions, $G$-invex functions, and type I functions. By using these new concepts, we formulate and prove the sufficient optimality conditions for the considered problem (GMP)${}_{h,\phi }$. In addition, we investigate a dual problem of Mond–Weir type, called (GMWD)${}_{h,\phi }$, and establish several duality results.

The idea behind the proposed queueing model is its adequacy in daily existent workplaces like hospitals, banks, and multinational companies where the service quality of computer networks and telecommunication systems are to be improved. The study consists of an $M^X/G/1$ retrial G-queue with preemptive priority arrivals where the service provider takes a working vacation and the vacation may get interrupted. The novelty of the proposed model lies as it anticipates state-dependent arrivals wherein unsatisfied clients may come up with immediate Bernoulli feedback. Also, the concept of preemptive priority is included which can be seen in various service systems of day-to-day life. The supplementary variable technique (SVT) is used to unfold the model and obtain the system performance indices and reliability measures. In continuation, the Grey Wolf Optimization (GWO) algorithm is used to obtain the optimal working vacation service rate and retrial rate under two scalar optimization problems where the system works under minimal cost and minimal awaiting time respectively. Lastly, the Pareto optimal solution of a bi-objective problem is also presented.

In this work, a new class of general contractive mappings, with the name Jaggi-Suzuki-type hybrid ($K$-$\alpha$-$\varphi$)-contractive mapping is discussed in metric space equipped with a graph and new criteria for which the mapping is a Picard operator are studied. The superiority of this type of contractive mapping lies in the fact that its contractive inequality can be fixed in different ways, depending on the specified constants. Substantial illustrations are furnished to validate the axioms of our obtained ideas and to show their difference from the existing concepts. Supplementarily, some corollaries which collapse our obtained notion to recently propounded results in the literature are brought out and analysed.

With the advent of quantum computers, many quantum computing algorithms are being developed. Solving linear systems is one of the most fundamental problems in modern science and engineering. The Harrow Hassidim-Lloyd algorithm, a monumental quantum algorithm for solving linear systems on gate model quantum computers, was invented and several advanced variations have been developed. The algorithm was difficult to apply to general linear equations because it required various conditions for the matrix. In this paper, we introduce a new algorithm that can be applied to all linear systems. For a given general square matrix $A\in R^{n\times n}$ and a vector $\overrightarrow{b}\in R^n$, we will find quadratic unconstrained binary optimization (QUBO) models for a vector $\overrightarrow{x}\in R^n$ that satisfies $A\overrightarrow{x}=\overrightarrow{b}$. To formulate QUBO models for solving linear systems, we made use of a linear least-square problem with binary representation of the solution. We validated those QUBO models on the d-Wave system and discussed the results. For a simple system, we provide a Python code to calculate the matrix characterizing the relationship between the variables, and to print the test code that can be used directly in the d-Wave system.

Hydrocarbons are one of the subclasses of organic compounds that comprise exactly of hydrogen and carbon. Alkanes are one of the types of hydrocarbons that have chemical formula $C_n{H}_{2n+2}$. Isomers are molecules with identical chemical formula but different structural arrangements, leading to variations in their spectral properties as their corresponding molecular graphs also differ in structure. This exploration is motivated by the understanding that variations in structural configurations manifest as differences in spectral properties, as evidenced by alterations in their respective molecular graphs. Alkanes with ten carbon atoms are called decanes. Our study employs a multifaceted approach, encompassing the determination of spectral properties and the calculation of eigenvalue-based entropy for the $C_{10}{H}_{22}$ decane isomers. This analysis is undertaken with the goal of unravelling the intricate relationships between structural variations and corresponding spectral bounds. Notably, our investigation extends beyond the realm of molecular structures to draw connections with physico-chemical properties. Through meticulous comparison of the obtained spectral data with the known characteristics of $C_{10}{H}_{22}$ isomers, we unveil interesting correlations among the characteristics. We establish that the spectral gap, a key parameter in our study, intriguingly exhibits a maximal correlation with the refractive index of the isomers. This finding not only enhances our understanding of the spectral intricacies of decane isomers but also underscores the practical implications of such research. The correlation between spectral gap and refractive index opens avenues for predicting and manipulating the optical properties of hydrocarbons, offering potential applications in diverse fields, from materials science to optics. In essence, this study bridges the gap between molecular structure and macroscopic properties, shedding light on the intricate interplay between isomeric variations and their consequential effects on spectral characteristics.

This paper proposes a novel controller optimization of boost converter by tunning two controllers of voltage and current in PV (Photovoltaic) boost converters: Sliding Mode Control (SMC) or Sliding Mode plus Proportional-Integrative. Genetic Algorithm (GA) optimization is applied in a Internet of Things (IoT) context, in which the server side consists of running the GA and thereafter used to tune the SMC and SMPIC of the PV plant boost converter. Communication between the IoT (PV plant) and cloud server comprises to the acquired currents and voltages from PV to the server and controllers parameters from server to IoT. Data from the IoT is applied to calculate the fitness function for a given solution, which learns the solar plant (machine learning). Experimental results using hardware are considered, in order to evaluate the performance, and results are compared between heuristic and deterministic parameters from SMC or SMPIC, proving the reduction of overshoot and settling time.

Models of nonlinear partial differential equations are essential in the theoretical sciences, especially Physics and Mathematics. It is exceedingly challenging to arrive at an analytical solution. For this reason, scientists are constantly looking for new computational techniques. For specific nonlinear mathematical models, such as the extensively researched Hunter-Saxton equation (HSE), Benjamin-Bona-Mahony equation (BBME), and Klein-Gordon equation (KGE), we are putting forth a novel and effective graph theoretic polynomial method named the Hosoya polynomial collocation method (HPCM). The orthonormalized Hosoya polynomials of the path graph and their operational matrices serve as the functional foundation for the new HPCM. The considered nonlinear models were transformed into a system of nonlinear algebraic equations using the operational matrix approximation and an appropriate collocation approach to reach the approximate solution. Six numerical examples verified the HPCM's productivity. Compared to the current numerical approaches in the literature, the results are significantly more efficient and almost match the analytical solution.

Linear Quadratic Regulator is one of the most common ways to control a linear system. Despite Linear Quadratic Regulator’s (LQR) strong performance and solid resilience, developing these controllers have been challenging, largely because there is no reliable way to choose the $Q$ and $R$ weighing matrices. In this regard a deterministic method is used for choosing them in this paper, providing the designers a precise control over performance variables. An Artificial Bee Colony (ABC) optimisation is also used to find the sub-optimal gain matrices along with an analytical approach based on neural networks. A comparative study of the three approaches is performed using MATLAB simulations. These three approaches are applied on an inverted pendulum–cart system due to its complexity and dexterity. The results show that all the three methods show comparable performances with the proposed analytical method being slightly better in terms of transient characteristics.

The development of cyber-physical systems entails the growth and diversity of malware, which increases the scale of cybersecurity threats. Attackers use malicious software to compromise various components of cyber-physical systems. Existing technologies make it possible to reduce the risk of malware infection using vulnerability and intrusion scanners, network analyzers, and other tools. However, there is no perfect protection against the increasingly sophisticated types of malware. The goal of this research is to solve this problem by combining different visual representations of malware and detection models based on transfer learning. This method considers two pre-trained deep neural network models (AlexNet and MobileNet) that are capable of differentiating various malware families using grayscale images. Radon transform is applied to the resulting grayscale malware images to improve the classification accuracy of the new malware binaries. The proposed model is evaluated using three datasets (Microsoft Malware Classification, IoT_Malware and MalNet-Image datasets). The results show the superiority of the proposed model based on transfer learning over other methods in terms of the efficiency of classifying malware families aimed at infecting cyber-physical systems.

The forthcoming era of industrialization is marked by intelligent production, with the manufacturing industry and the Industrial Internet of Things (IIoT) driving the creation and innovation of new concepts and systems. One innovative solution addressing the demands of industrial settings is the time-slotted channel hopping (TSCH) protocol, known for its ultra-low power consumption and reliable communication. However, the scheduling mechanism at the core of TSCH requires substantial time for every node. In this study, we propose a sustainable scheduling method for Industrial IoT networks called Linguistic Interval Type 2 Fuzzy Logic System (LIT2FLS) based Priority Aware TSCH (LIT2FLPAS). The suggested method aims to promote service diversification to extend battery life and reduce delay. The significance of each node is determined by the LIT2FLS method, which allocates the required number of slots based on priority and the volume of generated traffic. We compare our proposed LIT2FLPAS algorithm with two state-of-the-art scheduling functions, Scheduling Function-Zero (SF0) and Low Latency Scheduling Function (LLSF). The results demonstrate that LIT2FLPAS exhibits superior adaptability compared to SF0 and LLSF. It provides collision-free communications and significantly enhances the duty cycle by 8%, battery life by 30.32%, and packet loss ratio by 95% while reducing latency by 75.69% and collisions by 91%. These findings underscore the effectiveness of the LIT2FLPAS algorithm in addressing the specific challenges of industrial IoT networks, offering improved performance metrics and robust communication in dynamic industrial environments. The proposed method presents a promising solution for achieving efficient and reliable communication in the evolving landscape of intelligent industrial production.