Rough Environment Research Articles

Reliability allocation method combined with real working conditions under rough probabilistic linguistic environment

Reliability allocation method combined with real working conditions under rough probabilistic linguistic environment

A research on similarity law for combined ultimate bending and torsional strength

ABSTRACT As basic structural components of the ship hull, stiffened plates suffer both thrust and shear loads while ships sail in rough sea environments. In order to guide the scaled model design effectively, the similarity law is investigated in this paper. The parameters that determine the nonlinear behaviour of the stiffened plate under compression and shear loading are defined and the nonlinear similarity method is improved. This method is applied to design a scaled model under combined bending and torsional moment, and the collapse test is conducted to research the collapse characteristics. The collapse behaviour of actual ship structures under combined loads predicted by the model test is compared with that obtained by the nonlinear finite element analysis. The good agreement represents the capability of the newly improved nonlinear similarity law. The present research provides a proper way to investigate the collapse characteristics of actual ship structures through a model test.

중학교 음악교사의 원격수업 실행에 관한 질적 연구

Objectives The purpose of this study is to analyze the music teachers’ stories about ‘how to teach regular middle school music curriculum and what to experience’ in the situation of the COVID-19 Pandemic and to suggest the implications for how to improve the online music class.
 Methods Based on the narrative inquiry method, this study focused on exploring the meaning, that is, knowledge, created by collecting stories about non-face-to-face remote classes of middle school music teachers. For this study, three middle school music teachers in Seoul, who are guiding the music curriculum of the revised 2015 curriculum in middle schools, were selected as study participants. Although it was difficult to select research participants for in-depth interviews with middle school music remote classes, the selection of research participants was centered on experienced teachers with a lot of educational experience. In-depth interviews were conducted as a qualitative research method of Hatch, and the results were analyzed.
 Results First, the music teachers said that they taught students music theory and music appreciation-oriented learning contents, one-way online classes, and reduced evaluation. Second, the stories about what music teachers experienced showed the difficulties in the urgent and rough music online class environment, using unfamiliar music platforms and making learning contents and frequently changed academic schedules.
 Conclusions Through this study, we can derive suggestions on how to improve the online music class of middle school music teachers as follows: First, music teachers should enhance technological capabilities for Digital·AI devices. Second, Music teachers should be able to produce music content for feedback and cooperative communication. Third, music teachers should develop evaluation content tools centered on each student's development process.

A Multimodal Data Harness Approach of Mobile Sensors Trajectory Planning for Target Tracking

The future Internet comprising multimodal mobile wireless sensors (MMWSs) dramatically increases the data dimension and complexity compared to the traditional homogeneous networks. It consists of various types of nodes that can be deployed in different spatial domains to form the space, air, ground, and ocean integrated network (SAGOI-Net). A key challenge to IoT with SAGOI-Net is harnessing the real-time data from these sensors to obtain target characteristics and realize target tracking, especially in rough environment with obstacles and enemy threats. In this paper, we propose an approach -fuzzy logic and flocking control under extended kalman filter (EKFFL) to provide sensor nodes trajectory formation. With the tools from control systems and signal processing, this approach improves the commonly-adopted flocking control algorithm and fully employs multi modality of data. Compared with the modified kalman consistency filter (KCF) approach, the proposed EKFFL achieves higher target tracking accuracy with a shorter time period of trajectory formation. The trade-off between the data delay and tracking accuracy is also analysed. Based on its advantages, the EKFFL approach can be applied in broad scenarios, such as unmanned border awareness systems, environmental monitoring and intelligent transportation.

Design and Fabrication of Bio-Inspired Robotic Systems for Developed Mobility and Functionality in Unstructured Environments

In turn, bio-inspired robotic systems have significantly contributed to the development of well-abled robots capable of moving and manipulating with ease in unknown environments. Imitating biological creatures in their mechanisms of adaptation and effectiveness of design gives these robots enhanced qualities of mobility, functionality, and resilience. In fact, major advances build on flexible joints, controlled underactuation, adaptive control, and sensory arrays of high quality, enabling these robots to traverse rough environments and perform high-precision operations inside the human body. For instance, snake-like robots should be able to move through narrow gaps and debris in some kind of search and rescue task much like a real snake, robotic fish can move effectively across underwater environments while monitoring water quality and marine life. These advancements in technology have broadened the sphere of application and effectiveness of robot applications, proving to be valuable tools in many areas, including disaster response, environmental monitoring, and space exploration. In this regard, one also realizes their significance due to the context of bio-inspired robots regarding the capability of data collection and analyses. The usual methods of data collection are generally limited in reach, recurrence, and resolution. Bio-inspired robots compensate for these inadequacies by continuously gathering various real-time data from environments not easily reachable for humans. More precise and detailed analysis of data resolution collected through these robots supports advanced techniques such as AI and machine learning. This is a very crucial capacity that is central to applications such as following wildlife, assessing the impacts of climate change, and monitoring pollution levels. The capacity of these robots to provide timely and accurate data ensures efforts in environmental management and conservation. The importance of bioinspired robotics, therefore, comes in the solutions associated with unstructured environments. Through the gains of nature, which have been exploited by evolution, these robots present new solutions that overcome the inability of classical robotic systems. The persistence in the performance and development of this field of science will provide ever-greater achievements, with very probably a very important role in the exploration, understanding, and interaction with the natural world played by bioinspired robotics.

Cascade Control of Active Heave Compensation Nonlinear System for Marine Crane

During the rough marine environment, heave compensation is used to offset the heave motion of the vessel when a marine crane lifts and lands the load. Thus, load motion and vessel motion are realized decoupled. In previous studies, the interference items such as hydraulic cylinder friction, underwater drag force and nonlinear friction in the active heave compensation system of a marine hydraulic crane are compensated as a concentrated interference force to be estimated. In this paper, we disassembled the interference items; the disturbance observer and adaptive rate are designed to estimate unmodeled disturbance force and system uncertain parameters, respectively; and we designed an active heave compensator with the adaptive nonlinear cascade controller which has the disturbance observer (DOB-ANCC). For the heave compensation of load displacement, this paper derived the control law of the nonlinear system model based on the backstepping method. The outer loop control is displacement control and the inner loop control is pressure control. The simulation verifies the effectiveness of the control strategy proposed in this paper and the availability of heave displacement compensation for a marine crane hoisting load. The compensation efficiency of the designed controller (DOB-ANCC) for the heave motion of the load can reach more than 95%, and the maximum displacement tracking error of the controller can reach ± 0.035 m.

A decision modeling approach for smart e-tourism data management applications based on spherical fuzzy rough environment

The technology deployment in smart e-tourism brings high potential in terms of customer data, events, reservations, and others. It acts as an effective and personalized guide to aid travelers. There is an increasing variety of smart e-tourism apps with multiple categories and criteria, but in terms of decision making, this presents a multicriteria complex problem to determine the best app from a group of available options with high criteria subjectivity. Literature reviews have evaluated and modeled the existing smart e-tourism apps alternatives, but informational uncertainty remains. The fuzzy sets and Multi-Attribute Decision Analysis (MADA) were used to handle the subjectivity issue. However, this process includes levels of uncertainty, which affects the decisions made and still an open issues. Spherical fuzzy rough sets (SFRSs) environment are useful in this situation for resolving fuzziness and ambiguity. This paper proposed a decision modeling approach for smart E-Tourism data management applications based on SFRSs environment. For methodology: firstly, a decision matrix is adopted for 5 different categories of Smart E-tourism’s system applications on the basis of the integrated 12 evaluation criteria. Secondly, a new formulation and development formulating a new extension of FWZIC, called a Spherical Fuzzy Rough-Weighted Zero-Inconsistency (SFR-WZIC), for weighting the smart key concept attributes involved in modeling smart e-tourism, whereas a new formulation and development for a new extension of FDOSM, called a Spherical Fuzzy Rough Decision by Opinion Score Method (SFR-DOSM), for modeling the applications of smart e-tourism per each e-tourism category; then, the new developments are integrated. The proposed methods were evaluated using systematic ranking and sensitivity analysis.

Biofilm-Based Biocatalysis for Galactooligosaccharides Production by the Surface Display of β-Galactosidase in Pichia pastoris

Galactooligosaccharides (GOS) are one of the most important functional oligosaccharide prebiotics. The surface display of enzymes was considered one of the most excellent strategies to obtain these products. However, a rough industrial environment would affect the biocatalytic process. The catalytic process could be efficiently improved using biofilm-based fermentation with high resistance and activity. Therefore, the combination of the surface display of β-galactosidase and biofilm formation in Pichia pastoris was constructed. The results showed that the catalytic conversion rate of GOS was up to 50.3% with the maximum enzyme activity of 5125 U/g by screening the anchorin, and the number of the continuous catalysis batches was up to 23 times. Thus, surface display based on biofilm-immobilized fermentation integrated catalysis and growth was a co-culture system, such that a dynamic equilibrium in the consolidated integrative process was achieved. This study provides the basis for developing biofilm-based surface display methods in P. pastoris during biochemical production processes.

An integrated interval type-2 fuzzy rough technique for emergency decision making

This paper aims to develop a hybrid emergency decision-making (EDM) method by combining best–worst method (BWM), multi-attributive border approximation area comparison (MABAC) and prospect theory (PT) in trapezoidal interval type-2 fuzzy rough (TrIT2FR) environment. In this hybrid method, the decision information is represented by trapezoidal interval type-2 fuzzy rough numbers (TrIT2FRNs). Firstly, this paper defines the TrIT2FRN and analyzes its desirable properties. Then, the TrIT2FR-BWM is developed to determine criteria weights. To develop the TrIT2FR-BWM, this paper completes the following three core issues: (i) propose an effective theorem to normalize the TrIT2FR weights; (ii) build a crisp programming model to transform the minmax objective of weight-determining model for the TrIT2FR-BWM; (iii) design a consistency ratio for the TrIT2FR-BWM to check the reliability of the determined criteria weights. Afterwards, this paper extends the classical MABAC into TrIT2FR environment to calculate the border approximation area (BAA). Subsequently, the PT is used to rank the alternatives, in which the calculated BAA is selected as the reference point. Lastly, the validity of the proposed method is certificated with a real site selection case of makeshift hospitals on COVID-19. Sensitivity analysis and comparative analyses are conducted to illustrate the robustness and superiorities of the proposed method. Some valuable results are summarized as follows: (i) the best alternative determined by the proposed method conforms with the actual selection result, (ii) the proposed models in the TrIT2FR-BWM have strong robustness, (iii) PT is helpful to improve the decision quality of EDM.

An Optimization Strategy for MADM Framework with Confidence Level Aggregation Operators under Probabilistic Neutrosophic Hesitant Fuzzy Rough Environment

In this research, we first offer unique notions of averaging and geometric aggregation operators with confidence level by employing a probabilistic neutrosophic hesitant fuzzy rough framework. Then, we look into other descriptions of the suggested operators, such as idempotency, boundedness, and monotonicity. Additionally, for the derived operators, we establish the score and accuracy functions. We also provide a novel approach to assessing the selection procedure for smart medical devices (SMDs). The selection criteria for SMDs are quite complex, which is the most noteworthy feature of this investigation. It is suggested that these processes be simulated using a method utilizing a hesitant fuzzy set, a rough set, and a probabilistic single-valued neutrosophics set. The proposed approach is employed in the decision-making process, while taking into consideration the decision-makers’ (DMs’) level of confidence in the data they have obtained in order to deal with ambiguity, incomplete data, and uncertainty in lower and upper approximations. The major goal was to outline the issue’s complexities in order to pique interest among experts in the health care sector and encourage them to evaluate SMDs using various evaluation standards. The analysis of the technique’s outcomes demonstrated that the rankings and the results themselves were adequate and trustworthy. The effectiveness of our suggested improvements is also demonstrated through a symmetrical analysis. The symmetry behavior shows that the current techniques address more complex and advanced data.

Pavement Surface Distress Detection Using Digital Image Processing Techniques

Road safety and pavement condition are considered top priorities in our civilized societies, and it’s important that the pavement condition remains in an excellent state for a long time. However, eventually, the pavement will get exposed to different types of distresses as a result of traffic loads, rough environment conditions, soil conditions, and underline subgrade. Therefore, to achieve the required standards for the pavement surface roads in our country and provide the best performance: detection and measurements of distresses extension must be included in maintenance preparation. This paper proposes a technique for crack detection based on digital image processing using a programming language called Matrix Laboratory known as MATLAB. The main target is to estimate the pavement’s length, width, and area by capturing the image using a digital camera with the required precautions and image implementation. Secondly, developing an image pre-processing operation to eliminate environmental interference as much as possible and subsequently use the image thresholding method to separate the pixels within the image into two groups to find the thresholding value for image binarization. The method successfully detects and removes the presence of unwanted objects in an image, even in difficult situations where surfaces are less visible. Verification showed good results with an excellent processing time, which can be considered an indicator of pavement crack parameters.

CAGE20-7 Cruise Report: Sediment and water column analyses around flares at Norskebanken, Hinlopen and offshore Prins Karls Forland

During the cruise CAGE 20-7 (November 2nd to 16th 2020), we observed and investigated a large active methane seepage area south from the Yermak plateau (Northern Svalbard), in particular to characterize the water column and the sediment near known flares in Norskebanken (Geissler et al., 2016). Some flares were targeted for gas composition, DNA and bubble size distribution. The combination of the R/V Kronprins Haakon and the ROV ÆGIR 6000 from the Norwegian Marine Robotics Laboratory at the University of Bergen allowed this investigation to happen in this rough environment, while collecting invaluable data from the ROV. The cruise may be known as: CAGE20_7

Utility Maximization in Multivariate Volterra Models

This paper is concerned with portfolio selection for an investor with power utility in multiasset financial markets in a rough stochastic environment. We investigate Merton’s portfolio problem for different multivariate Volterra models, covering the rough Heston model. First we consider a class of multivariate affine Volterra models introduced in [E. Abi Jaber, E. Miller, and H. Pham, SIAM J. Financial Math.., 12 (2021), pp. 369–409]. Based on the classical Wishart model described in [N. Bäuerle and Z. Li, J. Appl. Probab., 50 (2013), pp. 1025–1043], we then introduce a new matrix-valued stochastic volatility model, where the volatility is driven by a Volterra–Wishart process. Due to the non-Markovianity of the underlying processes, the classical stochastic control approach cannot be applied in these settings. To overcome this issue, we provide a verification argument using calculus of convolutions and resolvents. The resulting optimal strategy can then be expressed explicitly in terms of the solution of a multivariate Riccati–Volterra equation. We thus extend the results obtained by Han and Wong to the multivariate case, avoiding restrictions on the correlation structure linked to the martingale distortion transformation used in [B. Han and H. Y. Wong, Finance Res. Lett., 39 (2021)]. We also provide existence and uniqueness theorems for the occurring Volterra processes and illustrate our results with a numerical study.

Long term coupling of AE sensors in high temperature environment

Acoustic emission monitoring took place in a long-term test in a coal fired power plant in Germany for 4 years. The test object was a steel pipe with a ceramic matrix composite (CMC) jacket, which was installed in a bypass of a live steam line. The peak operating temperature was approx. 530 °C. The aim of the AE monitoring was to investigate damage evolution in the CMC jacket. The test pipe was removed from the power plant in 2021. Due to several reasons, the jacket did not experience high loads during operation and therefore information about damage mechanisms in the AE data is rare. However, a lot could be learned from this project concerning long term stability of the equipment in rough environment, data storage and filtering and about sensor long term coupling of AE sensors at high temperatures. The high temperature AE sensors were applied directly onto the surface of the steel pipe and the CMC jacket using a ceramic glue. Additionally, the sensors were held by stainless steel bands which were acoustically decoupled by mineral wool. The application of the sensors took place at a temperature of about 30 °C. It was not possible to check on the quality of the coupling during operation as the whole pipe was covered by insulation, including all sensors. Also, the sensors did not allow pulsing as test for the coupling quality. Effects like thermal expansion of sensors, CMC jacket and ceramic glue had to be considered. Additionally, the power plant adepts its electrical output to the demands by flexible operation. This leads to cyclic changes in temperature and a high amount of heating and cooling cycles, which affects the coupling condition of the AE sensors. During dismantling of the test pipe from the power plant, a visual inspection of the sensor coupling was possible. The condition of the sensor coupling was documented before and after the sensors were removed. Pencil lead breaks were performed to quantify the quality of coupling as well. It could be shown that long term coupling of AE sensors is possible even in high temperature environment combined with cyclic temperature changes.

Polarization Characteristics of Multi-Angle Visible Light of Oil Spill Target in Rough Water Environment

Polarization Characteristics of Multi-Angle Visible Light of Oil Spill Target in Rough Water Environment

Decision support algorithm under SV-neutrosophic hesitant fuzzy rough information with confidence level aggregation operators

<abstract><p>To deal with the uncertainty and ensure the sustainability of the manufacturing industry, we designed a multi criteria decision-making technique based on a list of unique operators for single-valued neutrosophic hesitant fuzzy rough (SV-NHFR) environments with a high confidence level. We show that, in contrast to the neutrosophic rough average and geometric aggregation operators, which are unable to take into account the level of experts' familiarity with examined objects for a preliminary evaluation, the neutrosophic average and geometric aggregation operators have a higher level of confidence in the fundamental idea of a more networked composition. A few of the essential qualities of new operators have also been covered. To illustrate the practical application of these operators, we have given an algorithm and a practical example. We have also created a manufacturing business model that takes sustainability into consideration and is based on the neutrosophic rough model. A symmetric comparative analysis is another tool we use to show the feasibility of our proposed enhancements.</p></abstract>

Comprehensive angular scattering distribution analysis for resource-efficient manufacturing

Most measuring principles for surface topography measurement cannot be broadly used within machine tools due to the rough environment featuring vibrations, dust, cooling liquid, or temperature gradients. Having to measure outside of the machine tool results in large quality control loops. The angular-resolved scattering light sensor is an established measuring principle which can be used directly in the manufacturing environment and thus allows small quality control loops and a resource-efficient manufacturing since deviations in the manufacturing process can be detected almost immediately. For the evaluation of the surface characteristics, the angular distribution of the surface within a lightspot is typically characterized by its center of gravity (M-parameter) and its variance (Aq-parameter) as described in the guideline VDA 2009. These two parameters provide only little information about the angular distribution and the workpiece quality and for certain, function-oriented applications and expanded data analysis can be beneficial to allow the monitoring in more manufacturing systems with high benefits for resource-efficient manufacturing. We suggest using additional parameters of the angular distribution and perform a correlation analysis with the machining parameters of an exemplified manufacturing process to find parameters that feature a more pronounced correlation with the process parameters than Aq and thus allow an improved monitoring of the manufacturing process in this paper. The example process chosen is a grinding process with different tool grit sizes, tool surface speeds, feed rates, and coolant air flow power. With this case study it can be demonstrated how a more comprehensive analysis of the scattering of surface angles can lead to an improved process monitoring since the correlation between the process parameters and the parameters of the angular distribution shows how sensitively different deviations in the process can be detected, e.g. when the grinding belt must be replaced. For an efficient implementation of quality control loops, simple and traceable monitoring parameters are essential. A smart evaluation of the measurement data allows an optimization of the manufacturing process and the associated quality control loops.

Group decision‐making analysis based on distance measures under rough environment

AbstractA rough set as a superset of a crisp set is a mathematical tool to cope with uncertainty using initial data without additional assumptions and pre‐defined parameters. Using the technique of upper and lower approximations, rough models provide a complete description of the problem. This paper aims to introduce the notion of distance function, which is a metric, in rough graphs. We establish formulae of distance function, degree and radius of certain products of rough graphs in terms of initial given rough graphs. We discuss the concepts of weak isomorphism and isomorphism in rough graphs, and describe the properties of isomorphic rough graphs. We demonstrate the significance and importance of the distance function with an application to a decision making problem concerning organ trafficking networks. An application of rough matrices in steam valve systems is discussed, and we show how they can help to identify the potential failures and risk components during the power generation process. The results obtained under the rough model are compared with existing extensions of graphs and fuzzy based approaches.

Algorithm to Correct Measurement Offsets Introduced by Inactive Elements of Transducer Arrays in Ultrasonic Flow Metering

Ultrasonic flow meters (UFMs) based on transducer arrays offer several advantages. With electronic beam steering, it is possible to tune the steering angle of the beam for optimal signal-tonoise ratio (SNR) upon reception. Moreover, multiple beams can be generated to propagate through different travel paths, covering a wider section of the flow profile. Furthermore, in a clamp-on configuration, UFMs based on transducer arrays can perform self-calibration. In this manner, userinput is minimized and measurement repeatability is increased. In practice, transducer array elements may break down. This could happen due to aging, exposure to rough environments, and/or rough mechanical contact. As a consequence of inactive array elements, the measured transit time difference contains two offsets. One offset originates from non-uniform spatial sampling of the generated wavefield. Another offset originates from the ill-defined beam propagating through a travel path different from the intended one. In this paper, an algorithm is proposed that corrects for both of these offsets. The algorithm also performs a filtering operation in the frequency-wavenumber domain of all spurious (i.e., flow-insensitive) wave modes. The advantage of implementing the proposed algorithm is demonstrated on simulations and measurements, showing improved accuracy and precision of the transit time differences compared to the values obtained when the algorithm is not applied. The proposed algorithm can be implemented in both in-line and clamp-on configuration of UFMs based on transducer arrays.

Active Traversability Learning via Risk-Aware Information Gathering for Planetary Exploration Rovers

Traversability prediction enables safe and efficient autonomous rover operation on deformable planetary surfaces. Revealing spatial distribution from terrain geometry to rover slip behavior is key to assessing prospective traversability, but is hindered by insufficient in situ measurements on hazardous states due to conservative rover traverses. To achieve a more accurate prediction, this letter proposes a framework that actively learns latent traversability by exploring informative terrain under the constraints of stochastic rover slip. With a Gaussian process (GP) modeling the spatial distribution, we devise an iterative two-stage framework that gradually refines the model estimation, combining risk-aware informative path planning and GP updates by taking in situ measurements. The path planning stage employs our designed sampling-based algorithm to generate informative trajectories with fault-tolerant risk inference, while the GP is cautiously updated with traverse data to avoid rover immobilization. Chance constraint formulation is exploited in the framework to infer the stochastic reachability of informative regions. Through GP estimates reducing uncertainty, the algorithm incrementally reaches informative yet hazardous states along feasible trajectories. Simulation studies in rough terrain environments demonstrate that the proposed framework gathers informative traverse data while averting rover stuck situations to estimate the latent traversability model.