Proposed Integration Method Research Articles

New Upgrade to Improve Operation of Conventional Grid-Connected Photovoltaic Systems

The incorporation of distributed generation with photovoltaic systems entails a drawback associated with intermittency in the generation capacity due to variations in the solar resource. In general, this aspect limits the level of penetration that this resource can have without producing an appreciable impact on the quality of the electrical supply. With the intention of reducing its intermittency, this paper presents the characterization of a methodology for maximizing grid-connected PV system operation under low-solar-radiation conditions. A new concept of a hybrid system based on a constant current source and capable of integrating different sources into a conventional grid-connected PV system is presented. Results of an experimental characterization of a low-voltage grid–PV system connection with a DC/DC converter for constant-current source application are shown in zero and non-zero radiation conditions. The results obtained demonstrate that the proposed integration method works efficiently without causing appreciable effects on the parameters that define the quality of the electrical supply. In this way, it is possible to efficiently incorporate another source of energy, taking advantage of the characteristics of the GCPVS without further interventions in the system. It is expected that this topology could help to integrate other generation and/or storage technologies into already existing PV systems, opening a wide field of research in the PV systems area.

A Multi-Satellite SBAS for Retrieving Long-Term Ground Displacement Time Series

Ground deformation is one of the crucial issues threatening many cities in both societal and economic aspects. Interferometric synthetic aperture radar (InSAR) has been widely used for deformation monitoring. Recently, there has been an increasing availability of massive archives of SAR images from various satellites or sensors. This paper introduces Multi-Satellite SBAS that exploits complementary information from different SAR data to generate integrated long-term ground displacement time series. The proposed method is employed to create the vertical displacement maps of Almokattam City in Egypt from 2000 to 2020. The experimental results are promising using ERS, ENVISAT ASAR, and Sentinel-1A displacement integration. There is a remarkable deformation in the vertical direction along the west area while the mean deformation velocity is −2.32 mm/year. Cross-validation confirms that the root mean square error (RMSE) did not exceed 2.8 mm/year. In addition, the research findings are comparable to those of the previous research in the study area. Consequently, the proposed integration method has great potential to generate displacement time series based on multi-satellite SAR data; however, it still requires further evaluation using field measurements.

Carbon capture integration in seismic interpretation: Advancing subsurface models for sustainable exploration

Sustainable exploration in the energy industry necessitates innovative approaches that address environmental concerns without compromising the efficiency (or robustness) of subsurface modeling. This paper explores the integration of carbon capture technologies into seismic interpretation as a means to advance subsurface models for sustainable exploration. The study leverages existing seismic interpretation techniques and incorporates carbon capture data to enhance the accuracy and environmental sustainability of exploration practices. The key concepts discussed include an overview of seismic interpretation methods, an introduction to carbon capture technologies, and the rationale for integrating carbon capture into seismic models. The practical application section provides insights into the proposed integration method, supported by real-world examples showcasing successful applications and potential benefits for sustainable exploration. Challenges and considerations in the integration process are identified, accompanied by strategies to overcome obstacles and considerations for industry-wide adoption. A detailed case example illustrates the step-by-step application of the integration, emphasizing its impact on subsurface models. The paper concludes with a forward-looking perspective, outlining potential technological advancements for further integration, suggesting areas for future research and development, and anticipating improvements in sustainability and exploration practices. This paper aims to contribute to the ongoing discourse on sustainable exploration by providing a practical framework for integrating carbon capture in seismic interpretation, paving the way for environmentally conscious and efficient subsurface modeling in the energy sector.

New Empirical Bayes Models to Jointly Analyze Multiple RNA-Sequencing Data in a Hypophosphatasia Disease Study.

Hypophosphatasia is a rare inherited metabolic disorder caused by the deficiency of tissue-nonspecific alkaline phosphatase. More severe and early onset cases present symptoms of muscle weakness, diminished motor coordination, and epileptic seizures. These neurological manifestations are poorly characterized. Thus, it is urgent to discover novel differentially expressed genes for investigating the genetic mechanisms underlying the neurological manifestations of hypophosphatasia. RNA-sequencing data offer a high-resolution and highly accurate transcript profile. In this study, we apply an empirical Bayes model to RNA-sequencing data acquired from the spinal cord and neocortex tissues of a mouse model, individually, to more accurately estimate the genetic effects without bias. More importantly, we further develop two integration methods, weighted gene approach and weighted Z method, to incorporate two RNA-sequencing data into a model for enhancing the effects of genetic markers in the diagnostics of hypophosphatasia disease. The simulation and real data analysis have demonstrated the effectiveness of our proposed integration methods, which can maximize genetic signals identified from the spinal cord and neocortex tissues, minimize the prediction error, and largely improve the prediction accuracy in risk prediction.

Optimizing AUV Navigation Using Factor Graphs with Side-Scan Sonar Integration

For seabed mapping, the prevalence of autonomous underwater vehicles (AUVs) employing side-scan sonar (SSS) necessitates robust navigation solutions. However, the positioning errors of traditional strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) systems accumulated significantly, further exacerbated by DVL’s susceptibility to failure in complex underwater conditions. This research proposes an integrated navigation approach that utilizes factor graph optimization (FGO) along with an improved pre-integration technique integrating SSS-derived position measurements. Firstly, the reliability of SSS image registration in the presence of strong noise and feature-poor environments is improved by replacing the feature-based methods with a Fourier-based method. Moreover, the high-precision inertial measurement unit (IMU) pre-integration method could correct the heading errors of SINS significantly by considering the Earth’s rotation. Finally, the AUV’s marine experimental results demonstrated that the proposed integration method not only offers improved SSS image registration and corrects initial heading discrepancies but also delivers greater system stability, particularly in instances of DVL data loss.

Robust Reinforcement Learning-based Vehicle Control with Object Avoidance

This paper presents a control design framework for robust reinforcement learning (RL) based vehicle control enhanced with object avoidance using a model predictive approach. The proposed integration method is applied for motion control of autonomous road vehicles. In the motion control, longitudinal and lateral dynamics are incorporated, and the high-performance motion of the vehicle, through the RL-based control agent is achieved. ’H.co based robust control is used as a baseline parallel to the RL-agent to ensure stable operation. The object avoidance and limited error path following are achieved using a model-based predictive algorithm. Finally, the control signals of the three controllers are combined using a supervisor layer, which solves a constrained optimization task to ensure high performance and safe motion. The effectiveness of the proposed control method using simulation scenarios is illustrated.

Real-Time Integration of Identification and Semiactive Optimization Control for Mass Damper-Building Combined Systems under Known/Unknown Seismic Excitations

The integration of structural identification and vibration optimal control has been studied. Since the semiactive optimization vibrational control of civil structures needs to be implemented by massive control devices such as mass dampers, it is necessary to investigate the real-time integration of identification and semiactive optimization vibration control for mass damper-building combined systems. However, there is a lack of such studies in the literature. In this paper, a methodology is presented for real-time integration of identification and semiactive optimization vibration control of the mass damper-building combined system under known/unknown seismic excitations. For the combined system under known seismic excitations, the identification is implemented by the extended Kalman filter (EKF) using only partial structural acceleration responses. The identified structural state and parameters of mass damper-building systems are integrated in real time for the optimal control of systems by the linear-quadratic regulator (LQR) control algorithm and the Hrovat semiactive optimization control strategy via semiactive optimization mass dampers (SAMD). Then, it is extended to the scenario of unknown seismic excitations. The partially measured structural acceleration responses are absolute ones in this case, so the generalized extended Kalman filter with unknown input (GEKF-UI) developed by the authors is used to identify the structural input-state parameters of the mass dampers-building combined systems. The identification results are also integrated in real time for the semiactive optimization control of the combined system via SAMD. Two numerical simulation examples are used to test the proposed integration methods. It is shown that the proposed integration methods can reach almost the same optimal control effects as the conventional semiactive optimization control with known parameters of the mass damper-building combined systems under known/unknown seismic excitations.

Neural-symbolic BDI-Agent as a Multi-Context System: A case study with negotiating agent

Intelligent systems are already being deployed in several fields (e.g., medicine, education, automation, or legal), providing relevant tools to assist in our daily tasks. It is claimed that the next step of AI is the integration of connectionist and symbolic methods. Connectionist methods embody knowledge by assigning numerical conductivities or weights to the connections inside a network of nodes. Symbolic methods work by carrying on a sequence of logic-like reasoning steps over a set of symbols consisting of language-like representations. Combining the strengths of connectionist and symbolic methods can allow the development of robust intelligent systems. We propose a neural-symbolic BDI-agent based Multi-Context Systems (MCS) model to integrate these two methods. Neural-symbolic area explores the effective integration of connectionist and symbolic methods, more precisely, learning and reasoning. MCSs allow the representation of information exchange among heterogeneous sources. BDI-agents offer robust and flexible behavior, rapid and modular development, intelligibility, and verifiability. In our work, MCSs represent the symbolic method and Neural Networks the connectionist method. We present a case study of how the proposed agent can be implemented using the Sigon framework. Sigon facilitates the development of MCS agents using a programming language-like paradigm. In this case study, we employed a trained MultiLayer Perceptron (MLP) neural network, enabling us to mitigate the necessity of modeling some hand-crafted rules in symbolic systems. We performed two experiments, in which the main goal was to analyze the impact of using a neural network during the agent’s decision-making. In the first experiment, we compare the proposed agent with 136 negotiating agents participating in the Automated Negotiating Agents Competition (ANAC) available on the GENIUS framework. In the second experiment, we compare our work with one similar to ours. The results show that our agent can adapt and achieve high utility functions for different situations. However, it is necessary to consider the computational cost of using the NN’s output in every reasoning cycle. The contributions of this paper are: (i) - integration of connectionist methods as part of the agent’s decision-making, enabling the development of agents in a modular and flexible fashion; (ii) - custom sensors to handle different data types; (iii) - practical implementation of the proposed integration method; and (iv) - experiments of the proposed integration method, focusing on mitigating the necessity of hard-coded and hand-crafted rules.

Identification of maintenance significant items for machine tools by integrating DEMATEL and MABAC with spherical fuzzy sets

The identification of maintenance significant items (MSIs), which is a multi-criteria decision-making (MCDM) process, is the primary phase of reliability centered maintenance. The identification process is typically vague and uncertain as a result of the structural complexity and functional diversity of machine tools. Therefore, there are limited effective methods for identifying MSIs in machine tools. In this study, we propose a novel MSI identification method that integrates two MCDM methods, namely the decision-making trial and evaluation laboratory (DEMATEL) and multi-attributive border approximation area comparison (MABAC). A novel extension of the fuzzy theory called the spherical fuzzy (SF) set concept is combined with the integrated DEMATEL-MABAC method. SF-DEMATEL is used to evaluate the criteria weights of machine tools. Then, based on the judgement information regarding alternatives under each criterion, SF-MABAC is used to identify and rank MSIs. The proposed integration method was applied to a CNC lathe with six criteria and 11 alternatives. Robustness analysis was conducted to verify the stability and effectiveness of the proposed method. The results demonstrate that the integration of DEMATEL and MABAC under the SF concept is a reasonable and applicable method for MSI identification. The results support engineers into reasonably identifying the significant maintenance items for machine tools.

Magnetic Integration for Multiple Resonant Converters

A universal integration method for the magnetic components (MCs) of multiple resonant converters is proposed in this article. Two rules for integration of multiple independent MCs are summarized, one of which is to keep functional independent of MCs after integration, while the other is to achieve lower loss and volume through integration. Following the proposed rules and by dividing an MC into winding-part and nonwinding-part, integration is achieved by sharing the nonwinding-part of the MCs. An input-series output-parallel (ISOP) resonant converter is taken as an example to show how integration is accomplished step by step. It is shown that, both volume and loss of the magnetic cores are reduced after integration of all the high frequency transformers and inductors. Detailed theoretical analysis, finite element analysis simulations and comparisons have been presented. A 600 V–800 V input, 28 V/2000 W-output ISOP resonant converter was built. Experimental results have been provided to verify the effectiveness and feasibility of the proposed integration methods.

In-situ integration and performance verification of large-scale PZT network for composite aerospace structure

Piezoelectric transducer (PZT) based structural health monitoring (SHM) technology has been proved to be effective in increasing safety and reliability of composite aircraft structures. However, the attachment of PZT network to the host structure is considered as a weak link when facing the long term durability requirement of aerospace SHM, which should be overcome for aerospace SHM. In this paper, a surface-mounted co-curing method is creatively proposed to realize in-situ integration of large-scale PZT network and composite structure. By jointly controlling curing temperature and pressure, the proposed method can realize integration of large-scale PZT network and structural surface with high reliability and performance consistency. Compared with the conventional integration methods, the proposed method does not affect the manufacturing process or reduce the mechanical property of structure, and can be easily implemented whenever the host structure is available. To evaluate the effectiveness of the proposed integration method, a large-scale PZT network with an overall dimension of 1100 mm × 600 mm is integrated with a carbon fiber composite panel of wing box. The integration caused influence on functional integrity is first assessed by performing electro-mechanical impedance based theoretical analysis and experimental investigation. Then guided wave signals of the integrated large-scale PZT network are also acquired and analyzed, which proves the good signal repeatability and consistency of the network. At last, the SHM performance of the network is verified by conducting impact damages on the composite panel and performing damage monitoring, experimental results show that accurate monitoring is realized.

An Integrated ANP and MARCOS for Green Supplier Selection: A Case Study on Contruction Industry

Raw material suppliers are important in production, especially in the construction industry. It is because suppliers determine the availability of raw materials in a project. The research aims to propose a new integrated procedure for Green Supplier Selection in the Construction Industry. The proposed integration method is Analytical Network Process (ANP) and Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) method. Both methods are Multi-Criteria Decision Making (MCDM) procedures. The ANP method is used to determine the weight of each criterion, and the MARCOS method is used to determine the weight of the best supplier. Seventeen criteria are used in supplier selection involving economic and environmental dimensions. The results showed that based on the ANP method, the most important criterion was the consistent quality of goods. Meanwhile, three suppliers were analyzed for selection based on the MARCOS procedure. The results show that the best supplier is supplier X with a utility function value of 0.749. These results show that the proposed procedure (integration of ANP and MARCOS) effectively solves Green Supplier Selection.

Remote Real-Time Optical Layers Performance Monitoring Using a Modern FPMT Technique Integrated with an EDFA Optical Amplifier

Fiber performance monitoring using modern online technologies in the next generation of intelligent optical networks allows for identifying the source of the degeneration and putting in protective steps to increase remote optical network stability & reliability. In this paper, the performance of the fiber performance monitoring tool (FPMT) technique was improved by integrating it with optical amplifier boards. In this regard, the improved technique detects optical layer events and all fiber soft and hard failures at the online remote rather than disrupting the data flow with a measurement accuracy for defect location of up to ~99.9%, small tolerance of up to ~1 m, the longest distance to detecting optical line defects of up to ~300km, and enhanced power budget for the system with optimum insertion-loss of up to ~0.0 dB. The proposed integration method provides better results with an excellent and efficient solution at fault location measurement & detection in real-time with good financial implications of the technique. The competitiveness of the improved technique over the actual optical networks has been successfully confirmed through application to Huawei labs infrastructure nodes and displayed experimental simulation results.

Integration of Robust Control with Reinforcement Learning for Safe Autonomous Vehicle Motion

This paper presents a control design framework for the integration of robust control and reinforcement learning-based (RL) control agent. The proposed integration method is applied for motion control of autonomous road vehicles, providing safe motion. In the integrated motion control, longitudinal and lateral dynamics are incorporated. The high-performance motion of the vehicle, e.g., high-velocity motion, path following, and reduction of lateral acceleration, through the RL-based control agent is achieved. The training through Proximal Policy Optimization during episodes is performed. Safe motion with guaranteed performances, i.e., keeping limits on lateral error, through the robust control and the supervisor is achieved. The robust control is designed through the H∞ method, and in the supervisor, a constrained quadratic programming task is performed. As a result, lateral and longitudinal control inputs of the vehicle are calculated by the integrated control system. The effectiveness of the proposed control method using simulation scenarios and test scenarios on a small-scaled test vehicle is illustrated.

Integrating Occupational Health and Safety Risk and Production Economics for Sustainable SME Growth

Small and medium enterprises (SMEs) in the manufacturing industry normally arise as a result of product innovations. Transiting and growing into large organizations is a critical process for the sustainable growth of SMEs, and this requires developing appropriate production systems. Such development focuses on production economics and the optimization of facility layout, production capacity, and machine utilization. These optimizations are usually achieved using discrete event simulation (DES). However, the DES primarily focuses on production optimization and is not formulated to give concurrent attention to occupational health and safety risks, where the workers’ occupational health and safety are also important to production systems; hence, sustainability issues arise. Until now, the production economics and workers’ health and safety are usually treated separately, and the health and safety aspects are often managed after the system has been developed. This brings increasing hazards in the production environment since adding new facilities to the system may introduce new hazards and cause further safety issues. The present paper proposes a methodology to solve the above concerns. Its key features are the use of a quality-of-life metric for determining the occupational health and safety risks of a particular work activity and the embedding thereof as a routine in DES for manufacturing plant simulation. One of the contributions of the proposed integration method is that it helps to enhance the development of production systems that support financial due diligence, as well as occupational health and safety (OHS) due diligence. This is particularly important to SMEs in the manufacturing industries, where growth comes with particular sustainability challenges related to the need to develop more systematic operational and safety management systems.

Decoupled Magnetic Integration of Symmetrical LCL Filter With a Common-Mode Inductor for Single-Phase Grid-Connected Converters

High-frequency power electronic devices in grid-connected converters excited vast switching harmonics and electromagnetic interference (EMI) noises. Conventional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -type harmonic filters can well attenuate switching harmonics, but have a poor EMI suppression capability, especially for common-mode (CM) noise. Symmetrical filter structure and an additional EMI filter are generally needed to effectively suppress EMI in power converters, but this will inevitably increase the size and cost of the output filter. In order to tackle this issue without sacrificing the filter performance, this article proposes a decoupled magnetic integration of symmetrical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> filters with a CM inductor for single-phase grid-connected converters. The harmonic inductors of a symmetrical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> filter and a CM inductor are integrated on the side-limbs and middle-limb of an EE-type magnetic core, respectively. Due to the decoupled magnetic structure and the reasonable assignment of the windings, the magnetic coupling between converter-side inductors and grid-side inductors can be greatly canceled to improve the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> filters on the basis of the integration of CM inductor to suppress CM EMI. The detailed design guidance of the proposed integration method is presented. A single-phase decoupled magnetic integration filter is fabricated and tested, and the experimental results verify the effectiveness and feasibility of the proposed method.

Accuracy of integration of dental cast and cephalograms compared with cone-beam computed tomography: a comparative study.

This study proposes a method that integrates maxillary dental cast and cephalograms and evaluates its accuracy compared with cone-beam computed tomography (CBCT) scans. The study sample comprised 20 adult patients with records of dental casts, cephalograms, and craniofacial CBCT scans. The maxillary dental cast was integrated with lateral and frontal cephalograms based on best-fit registration of palatal and dental outline curves from dental cast with cephalogram tracings. Linear measurement was conducted to assess the intra- and inter-examiner reproducibility of the proposed integration method using intraclass correlation coefficients; linear and angular measurements were conducted to assess its accuracy with CBCT scans as a standard reference. Paired t test, one sample t test, and mean ± standard deviation of the absolute value of difference were used to compare the integrated images and CBCT. The integration method showed good intra- and inter-examiner reproducibility (intraclass correlation coefficients > 0.98). The differences in linear and angular measurements between the integrated images and CBCT were not statistically significant but with a large deviation. When absolute value of difference was computed, the linear distance error was 0.51 ± 0.34mm, the tooth point coordinate errors in X, Y and Z axes were 0.22 ± 0.22, 0.38 ± 0.32 and 0.21 ± 0.21mm, respectively; the angular error in pitch, roll and yaw of the dental cast was 0.82 ± 0.51, 0.92 ± 0.59 and 0.80 ± 0.41 degree, respectively. The proposed method for integration of dental cast and cephalograms showed good reproducibility and acceptable accuracy compared with CBCT. It could be helpful for researchers to study three-dimensional tooth growth changes using the existing craniofacial growth data especially cephalograms.

Integration of Interturn Fault Diagnosis and Fault-Tolerant Control for PMSM Drive System

This article first studies the integration of interturn fault diagnosis and fault-tolerant (FT) control of permanent magnet synchronous machine (PMSM) drive system. In this article, interturn fault is diagnosed by zero-sequence voltage component and the torque ripple due to interturn fault is reduced by the current injection-based FT control strategy. The main novelty of this article is that the injection currents for FT control are directly calculated based on the already obtained information during the fault diagnosis process, thus reducing the complexity of FT control and making interturn fault diagnosis and FT control convenient integration. Both the simulation and experimental results validate the effectiveness of the proposed integration method for the PMSM drive system.

A variational integrator for the Discrete Element Method

A novel implicit integration scheme for the Discrete Element Method (DEM) based on the variational integrator approach is presented. The numerical solver provides a fully dynamical description that, notably, reduces to an energy minimisation scheme in the quasi-static limit. A detailed derivation of the numerical method is presented for the Hookean contact model and tested against an established open source DEM package that uses the velocity-Verlet integration scheme. These tests compare results for a single collision, long-term stability and statistical quantities of ensembles of particles. Numerically, the proposed integration method demonstrates equivalent accuracy to the velocity-Verlet method.

Robotics for control strategy optimization and simulation in automatic cigarette sorter

In the world of advancement and rapid growth, new retail marketization and distribution require a substantial degree of versatility and resilience in the manufacturing plant to accommodate demand transitions. A Digital Matching Structure (DMS) is built such that real-time data from the framework for processing the physical goods can be aggregated and then translated into the digital context. A collaborative design approach is implemented in the DMS to automate stacked cigarette labelling and sorting. The periodic optimal decisions can be achieved from the joint optimization model by obtaining online details from the specific unit for the management network and then feeding them to the quasi-physical simulation platform in the DMS for validation of results. A sorting robot built-in DMS can be configured to catch and sort cigarettes on a conveyor belt to ensure high performance and eliminate the possibility of manual sorting. Digital Matching Structure – Sorting Robot (DMS-SR) is the solution recommended for the proposed integration. This article explores how finely packaged cigarettes are handled on the conveyor belt and how the packaging line works to understand and filter the product. The proposed integration method is assessed using appropriate mathematical models for validating its consistency and verifying the accuracy of sorting. Eventually, the proposed autonomous system parameters have been enhanced by tests on a real machine to boost the sorting performance. It was found that better sorting could be obtained when the conveyor speed of around 0.36 m/s. When there is sufficient load, growing the momentum and shortening the declaration/accelerationtime will produce the optimum efficacy. The DMS-SR currently has a sorting output of about 1900 cigarettes per hour, highly precise.