The selection of a reference model (RM) for a Model-Reference Adaptive Control is one of the most important aspects of the synthesis process of the adaptive control system. In this paper, the four different implementations of RM are developed and investigated in an adaptive PMSM drive with variable moment of inertia. Adaptation mechanisms are based on the Widrow-Hoff rule (W-H) and the Adaptation Procedure for Optimization Algorithms (APOA). Inadequate order or inaccurate approximation of RM for the W-H rule may provide poor behavior and oscillations. The results prove that APOA is robust against an improper selection of RM and provides high-performance PMSM drive operation.

Face sketch synthesis (FSS) is considered as an image-to-image translation problem, where a face sketch is generated from an input face photo. FSS plays a vital role in video/image surveillance-based law enforcement. In this paper, motivated by the recent success of generative adversarial networks (GAN), we consider conditional GAN (cGAN) to approach the problem of face sketch synthesis. However, despite the powerful cGAN model’s ability to generate fine textures, low-quality inputs characterized by the facial sketches drawn by artists cannot offer realistic and faithful details and have unknown degradation due to the drawing process, while high-quality references are inaccessible or even unexistent. In this context, we propose an approach based on Generative Reference Prior (GRP) to improve the synthesized face sketch perception. Our proposed model, that we call cGAN-GRP, leverages diverse and rich priors encapsulated in a pre-trained face GAN for generating high-quality facial sketch synthesis. Extensive experiments on publicly available face databases using facial sketch recognition rate and image quality assessment metrics as criteria demonstrate the effectiveness of our proposed model compared to several state-of-the-art methods.

The traditional industrial robots come with the prime mover, i.e. Electric Motors (EM) which ranges from a few hundred too few kilo watts of power ratings. However, for autonomous robotic navigation systems, we require motors which are light weighted with the aspect of high torque and power density. This aspect is very critical, when the EMs in robotic navigations are subjected to harsh high temperature survival conditions, where the sustainability of the performance metrics of the electromagnetic system of the EMs degrade with the prevailing high temperature conditions. Hence, this research work address and formulate the design methodology to develop a 630 W High Temperature PMSM (HTPMSM) in the aspect of high torque and power density, which can be used for the autonomous robotic navigation systems under high temperature survival conditions of 200°C. Two types of rotor configurations i.e. the Surface Permanent Magnet type (SPM) and the Interior Permanent Magnet type (IPM) of HTPMSM are examined for its optimal electromagnetic metrics under the temperature conditions of 200°C. The 630 W HTPMSM is designed to deliver the rated torque of 2 Nm within the volumetric & diametric constraints of D x L which comes at 80 x 70 mm at the rated speed of 3000 rpm with the survival temperature of 200°C with the target efficiency of greater than 90%. The FEM based results are validated through the hardware prototypes for both SPM and IPM types, and the results confirm the effectiveness of the proposed design methodology of HTPMSM for sustainable autonomous robotic navigation applications.

Motion planning for autonomous vehicles relies heavily on perception and prediction results to find a safe, collision-free local trajectory that adheres to traffic rules. However, vehicle perception is frequently limited by occlusion, and the generation of safe local trajectories with restricted perception is a significant challenge in the field of motion planning. This paper introduces a collision avoidance trajectory planning algorithm that considers potential collision risks, within a hierarchical framework of sampling and optimization. The primary objective of this work is to generate trajectories that are safer and align better with human driver behavior while considering potential collision risks in occluded regions. Specifically, in occlusion scenarios, the state space is discretized, and a dynamic programming algorithm is used for a sampling-based search to obtain initial trajectories. Additionally, the concept of a driving risk field is introduced to describe potential collision risk elements within the human-vehicle-road environment. By drawing inspiration from graph search algorithms, potential collision risk areas are accurately described, and a cost function is proposed for evaluating potential risks in occluded regions. Drivers typically exhibit conservative and cautious driving behavior when navigating through occluded regions. The proposed algorithm not only prioritizes driving safety but also considers driving efficiency, thereby reducing the vehicle's conservativeness when passing through occlusions. The research results demonstrate that the ego vehicle can actively avoidblind spots and tends to move away from occluded regions, aligning more closely with human driver behavior.

In order to study the sensitivity of multiple karst cave factors on surface settlement during Tunnel Boring Machine(referred to TBM hereinafter) tunnelling, a three-dimensional numerical model is built by taking a subway project as an example and combining MIDAS GTS NX finite element software. Secondly, the influence of the radius, height, angle, vertical net distance, and horizontal distance of the karst cave on the maximum surface settlement is studied and sorted under the two working conditions of treatment and untreated using the grey correlation analysis method. Additionally, a multi-factor numerical model of the untreated karst cave is established. Finally, based on the preceding research, a multi-factor prediction model for the maximum surface settlement is proposed and tested. The results reveal that when the karst cave is not treated, the radius and height of the karst cave have a significant effect on the maximum surface settlement. After the cave treatment, the influence of the cave parameters on the maximum settlement of the surface is greatly reduced. The calculating modelcreated in this study offers excellent prediction accuracy and good adaptability.

The power sector confronts a crucial challenge in identifying sustainable and environmentally friendly energy carriers, with hydrogen emerging as a promising solution. This paper focuses on the modeling, analysis, and techno-economic evaluation of an independent photovoltaic (PV) system. The system is specifically designed to power industrial loads while simultaneously producing green hydrogen through water electrolysis. The emphasis is on utilizing renewable sources to generate hydrogen, particularly for fueling hydrogen-based cars. The study, conducted in Skikda, Algeria, involves a case study with thirty-two cars, each equipped with a 5 kg hydrogen storage tank. Employing an integrated approach that incorporates modeling, simulation, and optimization, the techno-economic analysis indicates that the proposed system provides a competitive, cost-effective, and environmentally friendly solution, with a rate of 0.239 $/kWh. The examined standalone PV system yields 24.5 GWh/year of electrical energy and produces 7584 kg/year of hydrogen. the findings highlight the potential of the proposed system to address the challenges in the power sector, offering a sustainable and efficient solution for bothelectricity generation and hydrogen production.

In this work, the level of influence of the posts published by famous people on social networks on the formation of the cryptocurrency exchange rate is investigated. Celebrities who are familiar with the financial industry, especially with the cryptocurrency market, or are somehow connected to a certain cryptocurrency, such as Elon Musk with Dogecoin, are chosen as experts whose influence through social media posts on cryptocurrency rates is examined. This research is conducted based on statistical analysis. Real cryptocurrency exchange rate forecasts for the selected time period and predicted ones for the same period, obtained using three algorithms, are utilized as a dataset. This paper uses methods such as statistical hypotheses regarding the significance of Spearman’s rank correlation coefficient and Pearson’s correlation. It is confirmed that the posts by famous people on social networks significantly affect the exchange rates of cryptocurrencies.

Energy storage systems (ESS) are indispensable in daily life and have two types that can offer high energy and high power density. Hybrid energy storage systems (HESS) are obtained by combining two or more energy storage units to benefit both types. Energy management systems (EMS) are essential in ensuring HESS's reliability, high performance, and efficiency. One of the most critical parameters for EMS is the battery state of health (SoH). Continuous monitoring of the SoH provides essential information regarding the system's status, detects unusual performance degradations and enables planned maintenance, prevents system failures, helps keep efficiency at a consistently high level, and helps ensure energy security by reducing downtime. The SoH parameter depends on parameters such as Depth of Discharge (DoD), charge and discharge rate (C-Rate), and temperature. Optimal values of these parameters directly affect the lifetime and operating performance of the battery. The proposed Adaptive Energy Management System (AEMS) uses the SoH parameter of the battery as the control input. It provides optimal control by dynamically updating the C-Rate and DoD parameters. In addition, the supercapacitor integrated into the system with filter-based power separation prevents deep discharge of the batteries. Under the proposed AEMS control, HESS has been observed to generate 6.31% more energy than a system relying solely on batteries. This beneficial relationship between supercapacitors and batteries efficiently managed by AEMS opens new possibilities for advanced energy management in applications ranging from electric vehicles to renewable energy storage systems.

Microstructure, mechanical and corrosion properties of as-cast pure zinc and its binary and ternary alloys with magnesium (Mg), and copper (Cu) additions were investigated. Analysis of microstructure conducted by scanning electron microscopy revealed that alloying additives contributed to decreasing average grain size compared to pure zinc. Corrosion rate was calculated based on immersion and potentiodynamic tests and its value was lower for materials with Cu content. Moreover, it was shown that the intermetallic phase, formed as a result of Mg addition, constitutes a specific place for corrosion. It was observed that a different type of strengthening was obtained depending on the additive used. The presence of the second phase with Mg improved the tensile strength of the Zn-based materials, while Cu dissolved in the solution had a positive effect on their elongation.

The performance of long-wave infrared (LWIR) x = 0.22 HgCdTe avalanche photodiodes (APDs) was presented. The dark current-voltage characteristics at temperatures 200 K, 230 K, and 300 K were measured and numerically simulated. Theoretical modeling was performed by the numerical Apsys platform (Crosslight). The effects of the tunneling currents and impact ionization in HgCdTe APDs were calculated. Dark currents exhibit peculiar features which were observed experimentally. The proper agreement between the theoretical and experimental characteristics allowed to determine the material parameters of the absorber was reached. The effect of the multiplication layer profile on the detector characteristics was observed but was found to be insignificant.

The present work investigated the effect of modifying an epoxy resin using two different modifiers. The mechanical and thermal properties were evaluated as a function of modifier type and content. The structure and morphology were also analyzed and related to the measured properties. Polyurethane (PUR) was used as a liquid modifier, while Cloisite Na+ and Nanomer I.28E are solid nanoparticles. Impact strength (IS) of hybrid nanocomposites based on 3.5 wt% PUR and 2 wt% Cloisite or 3.5wt% PUR and 1wt% Nanomer was maximally increased by 55% and 30% respectively compared to the virgin epoxy matrix, exceeding that of the two epoxy/nanoparticle binaries but not that of the epoxy/PUR binary. Furthermore, a maximum increase in IS of approximately 20% compared to the pristine matrix was obtained with the hybrid epoxy nanocomposite containing 0.5 wt% Cloisite and 1 wt% Nanomer, including a synergistic effect, due most likely to specific interactions between the nanoparticles and the epoxy matrix. The addition of polyurethane and nanoclays increased significantly the thermal stability of epoxy composites. However, DSC results showed that the addition of flexible polyurethane chains decreased the glass transition temperatures, while the softening point and the service temperature range of epoxy nanocomposites containing nanofillers were increased. FTIR analysis confirmed the occurrence of interaction between the epoxy matrix and added modifiers. All SEM micrographs showed significant roughness of the fracture surfaces with the formation of elongated platelets, explaining the increase in mechanical properties of the epoxy matrix.

Directionality of light and modelling effects impact lighting quality in interiors. The modelling effects depend on luminaires’ photometric characteristics and their layout but also on interior size and reflectance. The objective of this research was to evaluate lighting design limitations and impact of interior and luminaires’ characteristics on the modelling effects, as well as elaborate a prediction method of the modelling effects in interior lighting. The General Index of Modelling was used for the analysis of the modelling effects in interiors. The implementation of the research objectives was based on the simulation and statistical analysis. 432 situations, varied interior size and reflectance, lighting class, luminaire downward luminous intensity distribution and layout were considered. The results show that achieving the required range of the General Index of Modelling in interior lighting is substantially limited. Luminaires’ layout impacts the General Index of Modelling the most. The elaborated multiple linear regression models can have a practical use for interior lighting design and analysis in terms of obtaining therequired range of the General Index of Modelling.

Federated Learning is an upcoming concept used widely in distributed machine learning. Federated learning (FL) allows a large number of users to learn a single machine learning model together while the training data is stored on individual user devices. Nonetheless, federated learning lessens threats to data privacy. Based on iterative model averaging, our study suggests a feasible technique for the federated learning of deep networks with improved security and privacy. We also undertake a thorough empirical evaluation while taking various FL frameworks and averaging algorithms into consideration. Secure Multi Party Computation, Secure Aggregation, and Differential Privacy are implemented to improve the security and privacy in a federated learning environment. In spite of advancements, concerns over privacy remain in FL, as the weights or parameters of a trained model may reveal private information about the data used for training. Our work demonstrates that FL can be prone to label-flipping attack and a novel method to prevent label-flipping attack has been proposed. We compare standard federated model aggregation and optimization methods, FedAvg and FedProx using benchmark data sets. Experiments are implemented in two different FL frameworks - Flower and PySyft and the results are analysed. Our experiments confirm that classification accuracy increases in FL framework over a centralized model and the model performance is better after adding all the security and privacy algorithms. Our work has proved that deep learning models perform well in FL and also is secure.

To improve the dynamic adaptability and flexibility of process route during manufacturing, a dynamic optimization method of multi-process route based on improved ant colony algorithm driven by digital twin is proposed. Firstly, on the basis of part manufacturing features analysis, the machining methods of each process are selected, and the fuzzy precedence constraint relationship between machining metas and processes is constructed by intuitionistic fuzzy information. Then, the multi-objective optimization function driven by digital twin is established with the optimization objectives of least manufacturing cost and lowest carbon emission, also the ranking of processing methods is optimized by an improved adaptive ant colony algorithm to seek the optimal processing sequence. Finally, the transmission shaft of some equipment is taken as an engineering example forverification analysis, which shows that this method can obtain a process route that gets closer to practical production.

The interpretation of breast magnetic resonance imaging (MRI) in the healthcare field depends on the good knowledge and experience of radiologists. Recent developments in artificial intelligence (AI) have shown advances in the field of radiology. However, the desired levels have not been reached in the field of radiology yet. In this study, a novel model structure is proposed to characterize the diagnostic performance of AI technology for individual breast dynamic contrast material–enhanced (DCE) MRI sequences. In the proposed model structure, Inception-v3, EfficientNet-B3 and DenseNet-201 models were used as hybrids together with the Yolo-v3 algorithm to detect breast and cancer regions. In the proposed model, DCE-MRI sequences (T2, ADC, Diffusion, Non-Contrast Fat Non-Suppressed T1, Non-Contrast Fat Suppressed T1, Contrast Fat Suppressed T1, and Subtraction T1) were evaluated separately and validation was made, thus providing a unique perspective. According to the validation results, the model structure with the best performance was determined as Yolo-v3 + DenseNet-201. With this model structure, 92.41 accuracy, 0.5936 loss, 92.44% sensitivity, and 92.44% specificity rates were obtained. In addition, it was determined that the results obtained without using contrast material in the best model were 91.53% accuracy, 0.9646 loss, 92.19% sensitivity, and 92.19% specificity. Therefore, it is predicted that the need for contrast material use can bereduced with the help of this model structure.

This article examines in depth the most recent thermal testing techniques for lithium-ion batteries (LIBs). Temperature estimation circuits can be divided into six divisions based on modeling and calculation methods, including electrochemical computational modeling, equivalent electric circuit modeling (EECM), machine learning (ML), digital analysis, direct impedance measurement, and magnetic nanoparticles as a base. Complexity, accuracy, and computational cost-based EECM circuits are feasible. The accuracy, usability, and adaptability of diagrams produced using ML have the potential to be very high. However, both cannot anticipate low-cost integrated BMS live due to their high computational costs. An appropriate solution might be a hybrid strategy that combines EECM and ML.

This study aims to evaluate the effectiveness of machine learning (ML) models in predicting concrete damage using electromechanical impedance (EMI) data. From numerous experimental evidence, the damaged mortar sample with surface-mounted piezoelectric (PZT) material connected to the EMI response was assessed. This work involved the different ML models to identify the accurate model for concrete damage detection using EMI data. Each model has been evaluated with evaluation metrics with the prediction/true class and each class is classified into three levels for testing and trained data. Experimental findings indicate that as damage to the structure increases, the responsiveness of PZT decreases. Therefore, examined the ability of ML models trained on existing experimental data to predict concrete damage using the EMI data. The current work successfully identified the approximately close ML models for predicting damage detection in mortar samples. The proposed ML models not only streamline the identification of key input parameters with models but also offer cost-saving benefits by reducing the need for multiple trials in experiments. Lastly, the results demonstrate the capability of the model to produce precise predictions.

A companion robot is capable of performing a variety of activities and thus supporting the elderly and people withdisabilities. It should be able to overcome obstacles on its own, respond to what is happening around it in real-time, andcommunicate with its surroundings. It is particularly important to pay attention to these issues, as a companion robot is likely tobecome a participant in traffic. The aim of the research is to develop a mathematical model that takes into account the use of twonavigation solutions in the companion robot. Thanks to this, it will be possible to use the obtained mathematical relationships tocompare various types of navigation and make a rational choice, enabling the implementation of the assumed activities in aspecific external environment. What is new in this article is the analysis of several navigation methods and the presentation ofresearch carried out in real time using an actual robot.

The study concentrates on two different Genetic Programming approaches for determining Passenger Car Equivalent(PCE) values and observing the impact on capacity estimation at urban unsignalized intersections. Considering heterogeneoustraffic conditions, a new PCE value is introduced to encompass sustainable modes of public transit vehicles, specifically Slow-moving three-wheelers (SM3W), commonly known as E-Rickshaws. Since PCE value is considered an important parameter forcapacity calculations, the present study considered 14 unsignalized intersections located in Ranchi city of India. An automaticplate recognition system is employed to have the count of vehicular traffic. The methodologies include Age layered populationstructure genetic programming (ALPSGP), and the Offspring selection genetic programming (OSGP) approach that incorporatesstatic and dynamic variables. Based on the significance test and ranking of the Genetic programming (GP) models, the OSGPmodel is recommended as the most appropriate model for heterogeneous traffic. Sensitivity analysis reported that laggingheadway (����) is the most contributing factor in PCE estimation. The PCE value of SM3W is found to be 0.81 and that could beincorporated as a new classification of vehicles in Indo-HCM. It is observed that evaluated capacity based on OSGP’s PCEvalues performed admirably in both normal and congested traffic situations.

The reaction of alkalis with aggregate containing reactive forms of silica (ASR) plays a significant role in shaping the durability of concrete, as the strongly hygroscopic reaction products generated lead to internal stress, causing its expansion and cracking. This study presents an extended analysis of corrosive processes occurring in mortars with reactive natural aggregate from Poland, using computed tomography and scanning microscopy methods. Numerous cracks in the grains and the surrounding cementitious matrix were observed, indicating a high degree of advancement of corrosive processes. Over time, the proportion of pores with reduced sphericity increased, indicating ongoing degradation of the mortars. The usefulness of computed tomography in studying the progress of ASR was demonstrated. Scanning microscopy confirmed that the cause of mortar degradation is the formed ASR gel with a typical composition, located within the volume of reactive grains, cracks propagating into the cementitious matrix, and accumulated in air voids.

Direction-splitting implicit solvers employ the regular structure of the computational domain augmented with the splitting of the partial differential operator to deliver linear computational cost solvers for time-dependent simulations. The finite difference community originallye mployed this method to deliver fast solvers for PDE-based formulations. Later, this method was generalized into so-called variationals plitting. The tensor product structure of basis functions over regular computational meshes allows us to employ the Kronecker product structureo f the matrix and obtain linear computational cost factorization for finite element method simulations. These solvers are traditionally usedf or fast simulations over the structures preserving the tensor product regularity. Their applications are limited to regular problems and regularm odel parameters. This paper presents a generalization of the method to deal with non-regular material data in the variational splitting method. Namely, we can vary the material data with test functions to obtain a linear computational cost solver over a tensor product grid with nonregularm aterial data. Furthermore, as described by the Maxwell equations, we show how to incorporate this method into finite element methods imulations of non-stationary electromagnetic wave propagation over the human head with material data based on the three-dimensional MRI scan.

This study examined the self-sensing performance of metakaolin-based geopolymer paste samples activated by an alkali activator, which is a solution mixture of potassium hydroxide and silica fume (SF). Their piezoresistive response under repeated compressive loads was found to improve with increasing SF content. It was found from their XRD, SEM, and FTIR results that the change in the SF content did not alter the phase composition of the geopolymer matrices. The factorial change in the electrical resistivity of geopolymer samples with a maximum SF content was calculated to be about 0.024. Their pore solutions were detected to be mainly filled with potassium and silicon ions due to the use of higher concentrations of SF particles. The UV spectrophotometry measurements of this study confirm that all the geopolymer samples displayed semiconductor behaviour. The SF-derived alkaline activator can enable the self-sensing character of geopolymer-based concrete, which isenvironmentally friendly and has recently shown great potential to monitor structural integrity under applied external loads.

The intelligent automated store warehouse (iZMS) research and development project was created to meet the expectations of a modern automatic store. The project concerns the development of the concept and pilot implementation of an automated store warehouse adapted to the autonomous and automatic sales of goods selected by retail chains. One of the aims of the iZMS project is to develop a scalable solution that allows for the simple adaptation of the iZMS to the needs of a potential customer, taking into account their requirements in terms of the quantity and variety of assortment offered within the iZMS. An important requirement in the use of the iZMS system is minimizing the customer waiting time for purchased products. This problem is related, among others, to the placement of products on the shelves of racks and will be solved in the optimizing process. Running optimization tasks requires a simulator that will mimic the features of a physical device faster than in real time in order to generate many proposals of the allocation of goods on storage racks in the shortest possible time and choose the best one, guaranteeing the shortest picking time of a representative basket of goods. A numerical simulator was developed to model the physical structures of food storage equipment and then simulate the sales process. Among the results obtained, the most important are the time parameters of individual operations, which will ultimately be used to optimize the placement of goods on storage racks. After analyzing the needs resulting from the usage of the iZMS system, we decided to develop a dynamic, deterministic simulator with discrete objects and perform the simulation with a controlled time increment and, in some cases, to utilize elements of event-driven simulation, in which the flow of goods is simulated with first-in, first-out (FIFO) queues. Finally,verification of the numerical simulator with a physical model confirmed that it can be employed in optimization processes.

This paper addresses the problem of designing secure control for networked multi-agent systems (MASs) under Denial-of-Service (DoS) attacks. We propose a constructive design method based on the interaction topology. The MAS with a non-attack communication topology, modeled by quasi-Abelian Cayley graphs subject to DoS attacks, can be represented as a switched system. Using switching theory, we provide easily applicable sufficient conditions for the networked MAS to remain asymptotically stable despite DoS attacks. Our results are applicable to both continuoustime and discrete-time systems, as well as to discrete-time systems with variable steps or systems that combine discrete and continuous times.