Self-adaptive Process Research Articles

An artificial intelligence based self-adaptive dynamic process control system for enhancing in-situ bioremediation of benzene-contaminated groundwater – Part I methods

This study develops a new AI-based self-adaptive dynamic process control (SADPC) system based on stepwise inference combined with genetic algorithm optimization technologies, including a filtered-clustering inference prediction model (FCI simulator), a stepwise inference controller (SI emulator), a model predictive control controller, and two optimizers. This system effectively reflects the dynamics and complexity of the bioremediation process and controls the remediation system based on the feedback information. To achieve this goal, a statistical model for simulating the enhanced bioremediation process through the FCI simulator is proposed, which can predict the resulting contamination situation based on the previous contamination situation and pumping/injecting flow rate. Then a bridge is established through the SI emulator, which can generate a control action based on a given contamination situation. This suggested decision makers that guidelines and policies on remediation-oriented SADPC systems could be tentatively investigated, developed, and applied in the future effort.

Study on the self-adaptive process of droplet traffic to a perturbation of flow resistance at the arms of a micro-T-junction

The response of the water–oil droplet flow to a perturbation of flow resistance added at the arms of a bypassed micro-T-junction is studied through a numerical method. The capillary number of the continuous flow varies between 0.007 and 0.034. Once an additional flow resistance is introduced at the arms of the T-junction, the flow self-adapts to the perturbation and droplets show complex dynamics at the junction, including splitting, merging, and reshaping. During the self-adaptive process, the instantaneous splitting ratios of the dispersed flow show positive correlations with that of the continuous flow. The correlation coefficient reduces as the increase in the amplitude of the perturbation. The ensemble splitting ratios of them, however, are independent. It shows a rather simple law that the ensemble splitting ratios of oil anchor at around a constant value while the corresponding parameter of water varies from 0 to 1. It illustrates that the droplet flow splitting at a T-junction resembles a resilient system, the stiffness of which depends on the capillary number of the flow. The energy loss of the system induced by a perturbation of the flow resistance is self-reduced by just redistributing of droplets into the two arms.

Non-fibril amyloid aggregation at the air/water interface: self-adaptive pathway resulting in a 2D Janus nanofilm.

The amyloid states of proteins are implicated in several neurodegenerative diseases and bioadhesion processes. However, the classical amyloid fibrillization mechanism fails to adequately explain the formation of polymorphic aggregates and their adhesion to various surfaces. Herein, we report a non-fibril amyloid aggregation pathway, with disulfide-bond-reduced lysozyme (R-Lyz) as a model protein under quasi-physiological conditions. Very different from classical fibrillization, this pathway begins with the air-water interface (AWI) accelerated oligomerization of unfolded full-length protein, resulting in unique plate-like oligomers with self-adaptive ability, which can adjust their conformations to match various interfaces such as the asymmetric AWI and amyloid-protein film surface. The pathway enables a stepwise packing of the plate-like oligomers into a 2D Janus nanofilm, exhibiting a divergent distribution of hydrophilic/hydrophobic residues on opposite sides of the nanofilm. The resulting Janus nanofilm possesses a top-level Young's modulus (8.3 ± 0.6 GPa) among amyloid-based materials and exhibits adhesive strength two times higher (145 ± 81 kPa) than that of barnacle cement. Furthermore, we found that such an interface-directed pathway exists in several amyloidogenic proteins with a similar self-adaptive 2D-aggregation process, including bovine serum albumin, insulin, fibrinogen, hemoglobin, lactoferrin, and ovalbumin. Thus, our findings on the non-fibril self-adaptive mechanism for amyloid aggregation may shed light on polymorphic amyloid assembly and their adhesions through an alternative pathway.

Dynamic comprehensive quality assessment of post-harvest grape in different transportation chains using SAHP–CatBoost machine learning

Abstract Objectives Food quality assessment is critical for indicating the shelf-life and ensuring food safety or value. Due to high environmental sensitivity, the post-harvest quality of fresh fruit will undergo complex changes in the supply chain, with various dynamic quality-related features. It is difficult to efficiently and accurately extract comprehensive quality feature of post-harvest fruits from high-dimensional monitoring data with heterogeneous characteristics (numerical and categorical). Therefore, we proposed a dynamic comprehensive quality assessment method based on self-adaptive analytic hierarchy process (SAHP) integrated with the CatBoost model. Materials and Methods By adaptive weight optimization, the SAHP was utilized to analyze the multi-source quality information and obtain the quantized fusion value, as an output sample of CatBoost machine learning. Then, using heterogeneous monitoring data as input, the CatBoost model was directly trained through unbiased boosting with categorical features for dynamic assessment of overall quality status. Results Three quality index monitoring data sets for ‘Jufeng’ grape in different transportation chains (normal temperature, cold insulation, and cold chain) were individually constructed as the research samples. Furthermore, compared to other machine learning methods, the SAHP–CatBoost had more accurate results in comprehensive quality feature extraction. In actual transportation chains, the mean absolute error, mean absolute percentage error, and root mean squared error of dynamic comprehensive assessment were limited to 0.0044, 1.012%, and 0.0078, respectively. Conclusions The proposed method is efficient in handling heterogeneous monitoring data and extracting comprehensive quality information of post-harvest grape as a robust shelf-life indicator. It can reasonably guide post-harvest quality management to reduce food loss and improve economic benefits.

Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition

Early detection of defects, such as keyhole pores and cracks is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM) to prevent build failures. However, the complex melt pool behaviour cannot be adequately captured by conventional single-modal process monitoring approaches. This study introduces a multisensor fusion-based digital twin (MFDT) for localized quality prediction in the robotic L-DED process. The data used in multisensor fusion includes features extracted from a coaxial melt pool vision camera, a microphone, and an off-axis short wavelength infrared thermal camera. The key novelty of this work is a spatiotemporal data fusion method that synchronizes multisensor features with the real-time robot motion data to achieve localized quality prediction. Optical microscope (OM) images of the printed part are used to locate defect-free and defective regions (i.e., cracks and keyhole pores), which serve as ground truth labels for training supervised machine learning (ML) models for quality prediction. The trained ML model is then used to generate a virtual quality map that registers quality prediction outcomes within the 3D volume of the printed part, thus eliminating the need of physical inspections by destructive methods. Experiments show that the virtual quality map closely matches the actual quality observed by OM. Compared to traditional single-sensor-based quality prediction, the MFDT has achieved a significantly higher quality prediction accuracy (96%), a higher ROC-AUC score (99%), and a lower false alarm rate (4.4%). As a result, the MFDT is a more reliable method for defect prediction. The proposed MFDT also lays the groundwork for our future development of a self-adaptive hybrid processing strategy that combines machining with AM for defect removal and quality improvement.

Injective Programmable Proanthocyanidin-Coordinated Zinc-Based Composite Hydrogel for Infected Bone Repair.

Effectively integrating infection control and osteogenesis to promote infected bone repair is challenging. Herein, injective programmable proanthocyanidin (PC)-coordinated zinc-based composite hydrogels (ipPZCHs) are developed by compositing antimicrobial and antioxidant PC-coordinated zinc oxide (ZnO) microspheres with thioether-grafted sodium alginate (TSA), followed by calcium chloride (CaCl2 ) crosslinking. Responsive to the high endogenous reactive oxygen species (ROS) microenvironment in infected bone defects, the hydrophilicity of TSA can be significantly improved, to trigger the disintegration of ipPZCHs and the fast release of PC-coordinated ZnOs. This together with the easily dissociable PC-Zn2+ coordination induced fast release of antimicrobial zinc (Zn2+ ) with/without silver (Ag+ ) ions from PC-coordinated ZnOs (for Zn2+ , > 100 times that of pure ZnO) guarantees the strong antimicrobial activity of ipPZCHs. The exogenous ROS generated by ZnO and silver nanoparticles during the antimicrobial process further speeds up the disintegration of ipPZCHs, augmenting the antimicrobial efficacy. At the same time, ROS-responsive degradation/disintegration of ipPZCHs vacates space for bone ingrowth. The concurrently released strong antioxidant PC scavenges excess ROS thus enhances the immunomodulatory (in promoting the anti-inflammatory phenotype (M2) polarization of macrophages) and osteoinductive properties of Zn2+ , thus the infected bone repair is effectively promoted via the aforementioned programmable and self-adaptive processes.

Comprehensive safety risk evaluation of fireworks production enterprises using the frequency-based ANP and BPNN

The fireworks industry has long struggled with the problem of safety. Scientific, reasonable, and operable evaluation models are prerequisites of reducing risk. Based on the data from over 100 fireworks production safety accidents in China from 2010 to 2022, two evaluation models were established from the perspective of safety risk definition. Firstly, a weight calculation derivative method, the frequency-based analytic network process (ANP), was proposed creatively. This method optimized the importance ranking index calculation process in the ANP by considering the causal frequency of risk factors in the historical accident samples, thus determining how much each indicator affects the likelihood of accidents. Secondly, utilizing the historical accident samples as the dataset, a back propagation neural network (BPNN) model was developed to extract the mathematical relationship between each risk factor and the severity of accident consequence. Finally, the frequency-based ANP and BPNN models were combined to determine the safety risk level of the fireworks production enterprises. Meanwhile, the safety evaluation research samples were used as the comparison set for empirical study with historical accident samples, involving 100 fireworks production enterprises in China evaluated from 2017 to 2020. The significance result of zero shows that there is a statistically significant difference between the likelihood evaluation results of the accident and non-accident companies. Additionally, the severity evaluation model exhibits an excellent result, revealing a classification accuracy of 98.21 %, a mean square error of 8.97 × 10−4, a percent bias of 1.24 %, and a correlation coefficient and Nash-Sutcliffe efficiency coefficient both of 0.96. The frequency-based ANP and BPNN models integrate self-learning, self-adaptive, and fuzzy information processing, obtaining more accurate and objective evaluation results. This work provides a new strategy for the promotion and application of artificial intelligence in the field of safety risk evaluation, thus offering real-time safety risk evaluation and decision support of the safety management for the enterprises.

If it is bad, why don’t I quit? Algorithmic recommendation use strategy from folk theories

Users persist in utilizing algorithmic recommendations despite perceiving their adverse consequences, such as privacy invasion and filter bubbles. This behavior appears contradictory to the innate human inclination to seek benefits and avert disadvantages. Employing folk theories, this study conducted in-depth interviews with 24 users who acknowledged the detrimental effects of algorithms and maintained a non-positive attitude toward them but continued their usage. The findings revealed that users adhere to the principle of rational choice: they engage in positive risk aversion (confining their usage to entertainment domain and affirming their own abilities) and negative self-avoidance (acknowledging the inevitability of privacy invasion, imagining the additional costs of turning off algorithmic recommendations, and believing that everyone relies on algorithms, rendering the costs relatively inconspicuous). Throughout this process of self-adaptation, users assumed diverse roles (the inactive, active, resister, swayer) to achieve a harmonious state and sustain their reliance on algorithmic recommendations.

A framework for AI-based self-adaptive cyber-physical process systems

AbstractDigital transformation is both an opportunity and a challenge. To take advantage of this opportunity for humans and the environment, the transformation process must be understood as a design process that affects almost all areas of life. In this paper, we investigate AI-Based Self-Adaptive Cyber-Physical Process Systems (AI-CPPS) as an extension of the traditional CPS view. As contribution, we present a framework that addresses challenges that arise from recent literature. The aim of the AI-CPPS framework is to enable an adaptive integration of IoT environments with higher-level process-oriented systems. In addition, the framework integrates humans as actors into the system, which is often neglected by recent related approaches. The framework consists of three layers, i.e., processes, semantic modeling, and systems and actors, and we describe for each layer challenges and solution outlines for application. We also address the requirement to enable the integration of new networked devices under the premise of a targeted process that is optimally designed for humans, while profitably integrating AI and IoT. It is expected that AI-CPPS can contribute significantly to increasing sustainability and quality of life and offer solutions to pressing problems such as environmental protection, mobility, or demographic change. Thus, it is all the more important that the systems themselves do not become a driver of resource consumption.

Rational Design of Highly Active and Stable (Bi1−xAx)2(Fe1−yBy)4O9 Mullite for Neutral‐ and Alkaline‐Water Electrolysis

Complex oxides-based oxygen evolution reaction (OER) catalysts, such as perovskite, mullite and spinel etc. structured materials, have attracted tremendous interest due to their unique crystalline structure and surface state. However, it remains challenge to directly use them for OER because it is quite difficult to manipulate their activity and durability to satisfy the requirements of market which appreciate the cost and performance. Herein, a series of (Bi1−xAx)2(Fe1−yBy)4O9 mullite (A= La and Ca, B = Ni, Zn and Cu) were synthesized via a practical hydrothermal method that is suitable to industrialization. With the composition regulation of the A sites and B sites, the optimized (Bi1−xAx)2(Fe1−yBy)4O9 mullite (Bi2(Fe0.9Ni0.1)4O9) delivers a minimum over-potential of 421 mV and 307 mV to reach the current density 10 mA cm-2 with small Tafel slope of 68.2 mV dec−1 and 62.4 mV dec−1 in O2- saturated 1 M NaCl and 1 M KOH solution, respectively, suggesting a good OER activity in both neutral- and alkaline- conditions. Not only that, the Bi2(Fe0.9Ni0.1)4O9 shows an extraordinary cycle stability and long-term operation durability also. After 5000 times accelerate speed test (AST-LSV) and 50 h i-t measurements the Bi2(Fe0.9Ni0.1)4O9 shows only 1.50 mA cm−2(@10 mA cm−2) and 20.13 mA cm−2(80.26 mA cm−2 residual active) activity loss, respectively. Specially, the i-t measurement shows an increasing of current density platform ranging from 22.1 h to 29.7 h reflecting a novel self-adaptive process, which would be one of the reasons on the good durability of Bi2(Fe0.9Ni0.1)4O9. The current work paves a new avenue for precise control of the A/B-sites on surface (Bi1−xAx)2(Fe1−yBy)4O9 for high activity and durability OER electrocatalysts.

A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block

As an indispensable material for animals, plants and human beings, obtaining accurate water body information rapidly is of great significance to maintain the balance of ecosystems and ensure normal production and the life of human beings. Due to its independence of the time of day and the weather conditions, synthetic aperture radar (SAR) data have been increasingly applied in the extraction of water bodies. However, there is a great deal of speckle noise in SAR images, which seriously affect the extraction accuracy of water. At present, most of the processing methods are filtering methods, which will cause the loss of detailed information. Based on the characteristic of side-looking SAR, this paper proposed a self-adaptive thresholding approach for automatic water extraction based on an OTSU algorithm and distance block. In this method, the whole images were firstly divided into uniform image blocks through a distance layer which was produced by the distance to the orbit. Then, a self-adaptive processing was conducted for merging blocks. The OTSU algorithm was used to obtain a threshold for classification and the Jeffries–Matusita (JM) distance was calculated with the classification result. The merge processing continued until the separability of image blocks reached the maximum. Subsequently, we started from the next block to repeat the merger, and so on until all blocks were processed. Ten study areas around the world and the local Dongting Lake area were applied to test the feasibility of the proposed method. In comparison with five other global threshold segmentation algorithms such as the traditional OTSU, MOMENTS, MEAN, ISODATA and MINERROR, the proposed method obtains the highest overall accuracy (OA) and kappa coefficient (KC), while this approach also demonstrates greater robustness in the analysis of time series. The findings of this study offer an effective method to improve water detection accuracy as well as reducing the influence of speckle noise and retaining details in the image.

A systematic review on security and safety of self-adaptive systems

Context:Cyber–physical systems (CPS) are increasingly self-adaptive, i.e. they have the ability to introspect and change their behavior. This self-adaptation process must be considered when modeling the safety and security aspects of the system. Objective:This study collects and compares security attacks and safety hazards on self-adaptive systems (SAS) described in the literature. In addition, mitigation and treatment strategies, as well as the modeling and analysis approaches, are investigated. Method:We conducted a systematic literature review on 21 selected papers. The selection process included a database search on four scientific databases using a common search string (1430 papers), forward and backward snowballing (1402 papers), and filtering the results based on predefined inclusion and exclusion criteria. The coding scheme to analyze the content of the papers was obtained through research questions, existing domain-specific taxonomies, and open coding. Results:Safety and security are not jointly modeled in the context of self-adaptive systems. The adaptation process is often not considered in the attack and hazard analysis due to naïve assumptions and modeling. The proposed approaches are mostly verified and validated through simulation often using simple use cases and scenarios. Conclusion:A thorough and joint modeling approach for safety and security in self-adaptive systems is still an open challenge that needs to be addressed. Further work is needed to address the gap between safety and security modeling in self-adaptive systems.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

CuFe2O4 spinel synergize with Pd as a robust electrocatalyst for formate oxidation reaction

As an energy conversion device, direct formate fuel cell (DFFC) has attracted much attention due totheir security, affordability as well as high energy conversion efficiency. However, the whole performance of the DFFC is unsatisfactory because of the repaid activity loss induced by Hads-poisoning of anode Pd catalysts during the formate oxidation reaction (FOR). Herein, we induced a CuFe2O4 complex into Pd to enhance the activity and release the Hads-poisoning to prolong the durability. The results reflect that the *OH generation capacity of CuFe2O4 played an important role in the Hads-stripping (Pd-Hads + CuFe2O4-OHads → Pd + CuFe2O4 + H2O). After optimized PCFO-3 (Pd/CuFe2O4) not only show a super FOR activity of 4427 mA mgPd−1 mass activity and 243 mA cm−2 specific activity, but also show a novel cycle durability of 10,000 times CV cycles and 12 h long-term i-t operation with a termination activity of 26.95 mA mgPd−1. More interestingly, this PCFO-3 shows a self-adapting process which induced an activity enhancement of 27.6% due to the synergistic effect of CuFe2O4 and Pd. We believe this work paves a new avenue for matching counterparts to enhance the FOR activity and durability of Pd electro-catalysts cooperatively.

Advanced Electronic and Optoelectronic Sensors, Applications, Modelling and Industry 5.0 Perspectives

This review will focus on advances in electronic and optoelectronic technologies by through the analysis of a full research and industrial application scenario. Starting with the analysis of nanocomposite sensors, and electronic/optoelectronic/mechatronic systems, the review describes in detail the principles and the models for finding possible implementations of Industry 5.0 applications. The study then addresses production processes and advanced detection systems integrating Artificial Intelligence (AI) algorithms. Specifically, the review introduces new research topics in Industry 5.0 about AI self-adaptive systems and processes in electronics, robotics and production management. The paper proposes also new Business Process Modelling and Notation (BPMN) Process Mining (PM) workflows, and a simulation of a complex Industry 5.0 manufacturing framework. The performed simulation estimates the diffusion heat parameters of a hypothesized production-line layout, describing the information flux of the whole framework. The simulation enhances the technological key elements, enabling an industrial upscale in the next digital revolution. The discussed models are usable in management engineering and informatics engineering, as they merge the perspectives of advanced sensors with Industry 5.0 requirements. The goal of the paper is to provide concepts, research topics and elements to design advanced production network in manufacturing industry.

Self-Adaptive Electronic Structure of Amphoteric Conjugated Ligand-Modified 3 d Metal-C3 N4 Smart Electrocatalyst by pH Self-Response Realizing Electrocatalytic Self-Adjustment.

Constructing pH-responsive smart material provides a new opportunity to address the problem that traditional electrocatalysts cannot achieve both alkaline oxygen evolution reaction (OER) and acidic hydrogen evolution reaction (HER) activities. In this study, amphoteric conjugated ligand (2-aminoterephthalic acid, BDC-NH2 )-modified 3d metal-anchored graphitic carbon nitride (3d metal-C3 N4 ) smart electrocatalysts are constructed, and self-adaptation of the electronic structure is realized by self-response to pH stimulation, which results in self-adjustment of alkaline OER and acidic HER. Specifically, the amino and carboxyl functional groups in BDC-NH2 undergo protonation and deprotonation respectively under different pH stimulation to adapt to environmental changes. Through DFT calculations, the increase or decrease of electron delocalization range brought by the self-response characteristic is found to lead to redistribution of the Bader charge around the modified active sites. The OER and HER activities are greatly promoted roughly 4.8 and 8.5 times over Co-C3 N4 after BDC-NH2 -induced self-adaptive processes under different environments, arising from the reduced energy barrier of O* to OOH* and ΔGH* . Impressively, the proposed BDC-NH2 -induced smart regulation strategy is applicable to a series of 3d metal anchors for C3 N4 , including Co, Ni and Fe, providing a general structural upgrading method for constructing smart electrocatalytic systems.

A model for the bio-mechanical stimulus in bone remodelling as a diffusive signalling agent for bones reconstructed with bio-resorbable grafts

Bone remodelling is a self-adaptive process occurring in bone living tissue to optimize its mechanical response to environmental demands. Some cells, namely osteocytes, monitor this response and communicate with other cells, osteoblasts, and osteoclasts, in charge of synthesizing or reabsorbing solid bone in order to acquire the mechanical strength required for bone functioning. In this paper, we employ a diffusive model of a mechanical stimulus to describe at the organ level the mechanisms involved in the transduction of the information and activation of the remodelling process. This stimulus is produced after a bone deformation, and thus we assume it depends on strain energy density. The system that we analyse is a bone sample with a resorbable graft. Mainly, we focus on the interaction between the two components of the system. From a mechanical point of view, we consider a mixture of two solid phases described by a poro-viscoelastic model. The results show a general behaviour that matches the expected outcomes and confirms the usefulness of the proposed model.

Statistical perception of the chaotic fabrication error and the self-adaptive processing decision in ultra-precision optical polishing.

Subaperture polishing is a key technique for fabricating ultra-precision optics. However, the error source complexity in the polishing process creates large fabrication errors with chaotic characteristics that are difficult to predict using physical modelling. In this study, we first proved that the chaotic error is statistically predictable and developed a statistical chaotic-error perception (SCP) model. We confirmed that the coupling between the randomness characteristics of chaotic error (expectation and variance) and the polishing results follows an approximately linear relationship. Accordingly, the convolution fabrication formula based on the Preston equation was improved, and the form error evolution in each polishing cycle for various tools was quantitatively predicted. On this basis, a self-adaptive decision model that considers the chaotic-error influence was developed using the proposed mid- and low-spatial-frequency error criteria, which realises the automatic decision of the tool and processing parameters. An ultra-precision surface with equivalent accuracy can be stably realised via proper tool influence function (TIF) selection and modification, even for low-deterministic level tools. Experimental results indicated that the average prediction error in each convergence cycle was reduced to 6.14%. Without manual participation, the root mean square(RMS) of the surface figure of a ϕ100-mm flat mirror was converged to 1.788 nm with only robotic small-tool polishing, and that of a ϕ300-mm high-gradient ellipsoid mirror was converged to 0.008 λ. Additionally, the polishing efficiency was increased by 30% compared with that of manual polishing. The proposed SCP model offers insights that will help achieve advancement in the subaperture polishing process.

Северная Евразия на стыках планетарных геокультур: сопространственность и пограничность

Geocultural features and transformations of states and large regions determine the specifics of their geopolitical trajectories. Any large geoculture can be conceived as a planetary one, with its own planetary cartographies and imaginative patterns. Northern Eurasia can be considered as a field of intersection and interaction of various planetary geocultures that shape the prospects for terrestrial development. The planetary influence of a large local civilization is associated with the presence of an original planetary geocultural cartography of the imagination, with the possibility of successfully translating these large-scale geocultural images outside, into the zones of influence of other large terrestrial civilizations. Co-spatiality in its civilizational and geocultural dimension means the presence of multiple purposeful planetary thinking that unfolds large-scale spatial communication patterns. Any co-spatial civilization or geoculture is totally borderline – its borderline is due to the constant process of geocultural selfadaptation in interaction with other geocultures and civilizations. Meta-geoculture explores the genesis, formation and various transformations of planetary imaginative cartographies focused on achieving their respective geocultural, geopolitical and geo-ideological goals. Meta-geocultural studies of Northern Eurasia should be aimed at identifying, first of all, the planetary geocultural cartographies of the imagination. Northern Eurasia can be considered as a space of interaction between the Western Euro-African cartography of the imagination, based on the Euro-Indian (or Indo-European) meta-geographic axis, and the East Asian cartography of the imagination, which is closely correlated with the Russian-Chinese meta-geographic axis.

MUSIC-Based Super-Resolution CMP Velocity-Depth Analysis for Multilayer Cases

This letter proposes a multiple signal classification (MUSIC)-based algorithm to generate a super-resolution velocity-depth spectrum for precise subsurface multilayer analysis, which cannot be achieved by conventional Common MidPoint (CMP) velocity-depth analysis. A self-adaptive peak detection process and a MUSIC algorithm with a modified steering matrix are the key components of the proposed method. Both numerical simulation and experimentation are used to verify the feasibility of this study. Regarding both accuracy and resolution, the results show that the suggested method outperforms the conventional SAR-based method, as the layer information on the spectra is correctly located and strongly focused. Moreover, the proposed approach can distinguish between the layers that cause weak reflections and those that cause strong reflections. Compared to previous studies, the proposed method enables self-adaptive super-resolution imaging in ground penetrating radar (GPR) CMP subsurface layer analysis, and it has a great potential for usage in other GPR subsurface applications.

Improved spherical search with local distribution induced self-adaptation for hard non-convex optimization with and without constraints

Most metaheuristic optimizers rely heavily on precisely setting their control parameters and search operators to perform well. Considering the complexity of real-world problems, it is always preferable to adjust control parameter values automatically rather than clamping them to a fixed value. In recent years, Spherical Search (SS) has emerged as a population-based stochastic optimization method that exploits the concepts of random projection matrices in linear algebra. As a result of the success of SS in solving non-convex, real-parameter optimization problems of various complexity, we have significantly extended SS in this paper by introducing a set of new algorithms, collectively known as Self Adaptive Spherical Search (SASS). Our proposal aims to enhance the performance of SS by using different projection matrix schemes in conjunction with improved search-direction calculations and an adaptive modification of parameter values. In our proposed adaptation scheme, parameters are modified to relevant values by applying a self-adaptive process that does not rely upon prior knowledge of the correlation between the parameter values and characteristics of the problem space. Consequently, we may apply the algorithms to bound and nonlinearly constrained optimization problems. For the benchmark suites derived from the most recent IEEE Congress on Evolutionary Computation (CEC) competitions, simulation results indicate that the SASS family of algorithms performs better than or is comparable to state-of-the-art algorithms from the other paradigms concerning robustness and convergence.