Unknown Information Research Articles

Multi-Attribute Group Decision-Making Methods Based on Entropy Weights with q-Rung Picture Uncertain Linguistic Fuzzy Information

This paper introduces a new concept called q-rung picture uncertain linguistic fuzzy sets (q-RPULSs). These sets provide a reliable and comprehensive method for describing complex and uncertain decision-making information. In addition, q-RPULSs help to integrate the decision maker’s quantitative assessment ideas with qualitative assessment information. For the q-RPUL multi-attribute group decision-making problem with unknown weight information, an entropy-based fuzzy set method for q-rung picture uncertainty language is proposed. The method considers the interrelationships among attributes and builds a q-rung picture uncertain language model. In addition, the q-RPULMSM operator and its related properties are discussed in this paper. This operator enables the fusion of q-RPULSs and helps to reach consensus in decision-making scenarios. To demonstrate the validity of the methodology, we provide a real case study involving commodity selection. Based on this case study, the reasonableness and superiority of the method are evaluated, highlighting the practical advantages and applicability of q-RPULSs in decision-making processes.

Reminders of Japanese redress increase Asian American support for Black reparations

Informational interventions can shape policy attitudes, and in this study, we examined whether largely unknown information about past reparations payments toward one minoritized group would shape current policy judgments. In 1942, the U.S. government wrongfully relocated and imprisoned more than 120,000 Japanese Americans. In 1988, the government apologized and offered $20,000 USD in reparations payments. Japanese American redress is a recent, but not widely known, concrete example of communities who have successfully fought for reparative economic action. In two preregistered studies of online crowdsourced panels of Asian Americans (N = 329, N = 500), an intervention that raised awareness of this history of incarceration and redress increased support for reparations for Black Americans, relative to a control condition, and national polling data on support for reparations. Exploratory analyses revealed that the degree of learning about Japanese American redress in the intervention explained its impact on support for Black reparations. Future research should target representative samples to understand how education about past redress within one’s own social group affects support for reparative economic justice for others.

Attitude- and cost-driven consistency optimization model for decision-making with probabilistic linguistic preference relation

Probabilistic linguistic term set (PLTS) conveys the preference for a linguistic term by assigning a probability distribution, which can more correctly and flexibly reflect the decision-maker (DM)’s evaluation information and make the decision outcome more scientific and rational. Probabilistic linguistic preference relation (PLPR) allows the DM to represent decision information for pairwise comparison of alternatives with PLTS, allowing a more comprehensive representation of the DM’s valid preferences. Consistency is essential in the preference decision-making process and directly influences the dependability of the final results. Therefore, this paper develops a minimum cost consistency adjustment mechanism based on attitudes and emotions for solving the decision-making problem with PLPR. Firstly, a new PLTS normalization model is proposed to handle the unknown information, considering the DM’s preferred attitude and emotional tone. Secondly, the expected parametric multiplicative consistency index for PLPR is defined, and parameter settings improve the flexibility and relevance of the consistency. Then, the concepts of linguistic hesitancy, probabilistic uncertainty, and probabilistic incompleteness are defined for the PLTS that can reflect the psychological characteristics of DMs’ hesitancy; they are then used to measure hesitation and unit consistency adjustment costs. Next, a new distance measure is established between the two PLTSs to measure the difference between the preference information. Subsequently, a direction-guided minimum cost consistency optimization model with unacceptable consistency is constructed for the PLPR. Finally, the usefulness of the suggested decision-making method with PLPR is tested using a numerical example of talent selection, and parametric sensitivity analysis and comparative analysis are utilized to show the benefits of the suggested methodology.

Three Scattered Sunspot Records in the Archive of the Royal Astronomical Observatory of the Spanish Navy (1789–1790)

The recovery of records of past solar observations is crucial to understand the evolution of solar activity. In this context, three records of sunspot observations made at the Royal Observatory of the Spanish Navy in 1789 January and July, and 1790 June are shown in the present work. The aim of the observers who performed these sunspot observations was the discovery of intramercurial planets crossing the solar disk. For that reason, the observers only recorded round and dark spots and they may not have recorded all the sunspots that they observed. However, these records provide us with previously unknown information such as dates of active days (days with sunspots on the Sun) and position of sunspots.

A practical decision-making framework for highway transportation scheme selection based on the triangular fuzzy neutrosophic information

In recent years, due to the further development of the market economy, the internal competition in the large-cargo transportation industry has become increasingly fierce, and the profit space has been greatly compressed. Therefore, large-cargo logistics enterprises are paying more and more attention to the research of highway transportation route plan. The highway transportation scheme selection is looked as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic numbers (TFNN) grey relational analysis (TFNN-GRA) method is established based on the classical grey relational analysis (GRA) and triangular fuzzy neutrosophic sets (TFNSs) with completely unknown weight information. In order to obtain the weight values, the information Entropy is established to obtain the weight values based on the score and accuracy functions under TFNSs. Then, combining the traditional fuzzy GRA model with TFNSs information, the TFNN-GRA method is set up and the computing steps for MADM are established. Finally, a numerical example for highway transportation scheme selection was established and some comparisons are established to study the advantages of TFNN-GRA. The main contributions of this paper are established as follows: (1) the information Entropy is established to obtain the weight values based on the score and accuracy functions under TFNSs; (2) the TFNN-GRA method is established with completely unknown weight information. (2) the TFNN-GRA method is established and the computing steps for MADM are established. (3) Finally, a numerical example for highway transportation scheme selection was established and some comparisons is employed to study advantages of TFNN-GRA method.

Sampled-Data Output Feedback Control for Nonlinear Uncertain Systems Using Predictor-Based Continuous-Discrete Observer.

In this article, we investigate the problem of sampled-data robust output feedback control for a class of nonlinear uncertain systems with time-varying disturbance and measurement delay based on continuous-discrete observer. An augmented system that includes the nonlinear uncertain system and disturbance model is first found, and by using the delayed sampled-data output, we then propose a novel predictor-based continuous-discrete observer to estimate the unknown state and disturbance information. After that, in order to attenuate the undesirable influences of nonlinear uncertainties and disturbance, a sampled-data robust output feedback controller is developed based on disturbance/uncertainty estimation and attenuation technique. It shows that under the proposed control method, the states of overall hybrid nonlinear system can converge to a bounded region centered at the origin. The main benefit of the proposed control method is that in the presence of measurement delay, the influences of time-varying disturbance and nonlinear uncertainties can be effectively attenuated with the help of feedback domination method and prediction technique. Finally, the effectiveness of the proposed control method is demonstrated via the simulation results of a numerical example and a practical example.

Ionospheric irregularity reconstruction using multisource data fusion via deep learning

Abstract. Ionospheric sporadic E layers (Es) are intense plasma irregularities between 80 and 130 km in altitude and are generally unpredictable. Reconstructing the morphology of sporadic E layers is not only essential for understanding the nature of ionospheric irregularities and many other atmospheric coupling systems, but is also useful for solving a broad range of demands for reliable radio communication of many sectors reliant on ionosphere-dependent decision-making. Despite the efforts of many empirical and theoretical models, a predictive algorithm with both high accuracy and high efficiency is still lacking. Here we introduce a new approach for Sporadic E Layer Forecast using Artificial Neural Networks (SELF-ANN). The prediction engine is trained by fusing observational data from multiple sources, including a high-resolution ERA5 reanalysis dataset, Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) measurements, and integrated data from OMNIWeb. The results show that the model can effectively reconstruct the morphology of the ionospheric E layer with intraseasonal variability by learning complex patterns. The model obtains good performance and generalization capability by applying multiple evaluation criteria. The random forest algorithm used for preliminary processing shows that local time, altitude, longitude, and latitude are significantly essential for forecasting the E-layer region. Extensive evaluations based on ground-based observations demonstrate the superior utility of the model in dealing with unknown information. The presented framework will help us better understand the nature of the ionospheric irregularities, which is a fundamental challenge in upper-atmospheric and ionospheric physics. Moreover, the proposed SELF-ANN can make a significant contribution to the development of the prediction of ionospheric irregularities in the E layer, particularly when the formation mechanisms and evolution processes of the Es layer are not well understood.

A shale gas production prediction model based on masked convolutional neural network

Shale gas production prediction is of great significance for shale gas exploration and development, as it can optimize exploration strategies and guide adjustments to production parameters for both new and existing wells. However, the dynamic production characteristics of shale gas wells under the influence of multiple factors such as reservoirs, engineering, and production, exhibit complex nonlinear and non-stationary features, leading to low accuracy in predicting shale gas production. To address this issue, a novel masked convolutional neural network (M-CNN) based on masked autoencoders (MAE) is proposed for shale gas production prediction. First, high-dimensional shale gas production data are transformed into images with unknown information using an encoding structure, thereby converting the regression task into images generation task. Then, convolutional neural network is used for image restoration prediction, and the corresponding numerical values at the image positions are extracted as shale gas production prediction results. Specifically, dilated convolution and multi-scale residual structure (MSRS) are developed to improve the feature representation capability of the network. Meanwhile, convolutional block attention module (CBAM) is adopted to enhance the feature extraction ability of the M-CNN. The performance of our method is validated experimentally on shale gas production data of Changning (CN) block in China. The average RMSE, MRE, and R2 on the test sets are 0.211 (104m3/d), 10.9%, and 0.906, respectively, which is much lower than the traditional time series models. Experimental results demonstrate the effectiveness and superiority of the proposed M-CNN method for shale gas production prediction.

A pilot study on home range of female Chinese goral (Naemorhedus griseus): exploring GPS tracking data in a cliff landscape

ABSTRACT Home range size estimation is an important basis for developing effective conservation strategies and provides important insights into the study of animal behavior and spatial ecology. We analyze home range variations and the fitfulness of estimation methods on Chinese gorals (Naemorhedus griseus) living at a cliff landscape with GPS tracking data. We report annual, seasonal, and monthly home range sizes of five female Chinese gorals tracked from February 2015 to September 2018. We found variations existed in 95% seasonal home range sizes among estimation methods. We recommend that the α-LoCoH model be used in home range estimations, as this model appeared to more accurately reflect animal locations within the steep cliff habitat in our study area. Furthermore, more animals should be GPS tracked to ensure the validity of any interpretation concerning their home range size in a rugged habitat. This study was the first conducting home-range GPS tracking of Chinese gorals in their northern distribution range. Additionally, our findings provided previously unknown information regarding the home range traits of this endangered species, and they may contribute to the development of effective conservation strategies.

Research on Business Data Analysis and Mining based on Deep Learning

At present, the database system can efficiently realize the functions of data entry, query and statistics, but it can't find the rules of relationships in the database, and can't predict the future development trend according to the existing data. Using data mining technology, we can effectively find the regularity behind a large number of data, provide the hidden knowledge and means in the data, and eliminate the phenomenon of data "explosion but lack of knowledge". Based on DL (Deep Learning), this paper studies the analysis and mining of commercial data. In order to better assist investors to evaluate and make decisions on financial data, it puts forward the need to build a reliable and effective financial data forecasting model. In the original data obtained, it is necessary to interpolate by certain methods in data cleaning, and some repeated data will be duplicated. This paper analyzes and makes full use of these massive commercial information through DL, finds out the potential laws of business and market operation, and finds some unknown and hidden useful information from it, which helps enterprises to grasp the development direction of the industry and sales market from a macro perspective and better serve the vast number of consumers.

Phase Identification in Unobservable Distribution Systems

Accurate network and phase connectivity models are crucial to distribution system planning, operations, and situational awareness. In this regard, this paper proposes a novel phase identification approach for unbalanced distribution networks where phases are insufficiently labeled and measurements are limited (i.e., system is unobservable). The proposed approach utilizes the nodal voltage magnitude measurements for clustering the graph representing the phase connectivity information. The proposed approach consists of an optimization formulation that infers the connectivity of the entire multi-phase distribution network in a single shot. Simulation results on IEEE 37, IEEE 123 and EPRI Ckt5 bus test system illustrate the ability of the proposed approach to accurately identify the phase labels under varying availability of data, phase labels errors, and unknown phase connectivity information. The accuracy of the proposed approach is above 90% under these different scenarios and offers gain over 70% over existing state of the art spectral clustering based approaches for phase identification.

A Novel Distributed Fault Diagnosis Scheme Toward Open-Set Scenarios Based on Extreme Value Theory

Under closed-set scenarios (CSS), distributed modeling performs well in fault diagnosis of plant-wide industrial processes due to its flexibility and robustness. However, a more realistic scenario is often open, where unseen situations may arise unexpectedly, rendering existing methods infeasible. The advent of open-set recognition algorithms that can effectively distinguish known samples and reject unknown ones bridges this gap. Nevertheless, the poor scalability of these algorithms prevents them from being elegantly embedded in popular distributed modeling schemes, which hinders the implementation of plant-wide industrial process fault diagnosis toward open-set scenarios (OSS). In this work, we formulate a novel distributed fault diagnosis scheme toward OSS to solve this problem. Firstly, a mutual information-based local module decomposition and expansion strategy is proposed to minimize the loss of inter-module relevant information. Secondly, a novel generalized basic probability assignments generation technique based on extreme value theory is developed for modeling unknown information. It enables any classifier capable of probabilistic prediction to be applied to OSS and easily embedded in distributed modeling schemes. Finally, a conflict management scheme combining supervised and unsupervised is devised to address the vulnerability of the modified generalized combination rule to counter-intuitive results from fusing conflicting evidence. Experimental results on two plant-wide industrial process datasets demonstrate the proposed approach's feasibility and superiority.

Production performance, breeding practices and challenges of Holstein-Local crossbred cattle in some selected areas of Bangladesh

The aim of this study was to investigate productive and reproductive performances, current cattle breeding practices and its associated problems in different Holstein-Local (HF×L) crossbred genotypes under existing management condition of Bangladesh. Data on HF×L crossbred dairy cows were collected from the selected farmers of Natore, Sirajganj, Kishoreganj, Tangail and Gazipur districts while breeding bulls’ information were collected from different cattle breeding service providers. Genotype had significant effects on weight at first heat (WFH), mature body weight (MBW), age at first heat (AFH), age at first calving (AFC), post-partum heat period (PPHP), daily milk yield (DMY) and peak milk yield (PMY) (P<0.05) except the traits birth weight (BW) and service per conception (SPC). The HF75%×L25% crossbreds had significantly better AFH and AFC (P<0.001) than the three genetic groups having 50%, 62.5% and >75% HF inheritance. The highest DMY was found in >HF75%×L<25% (12.80±0.72 liters) and was lowest in HF50%×L50% (5.34±0.65 liters) crossbreds. At present 11 different graded breeding bulls have been used in artificial insemination (AI) program of Bangladesh. Use of heterogeneous breeding bulls (72.9%), AI technicians depended bull’s genotype selection (63%), absence of breeding data recording (89%) system and local market-based cow purchasing (83%) with unknown pedigree information were the major challenges identified for crossbred cattle development. Among the investigated samples, 27% cows suffered from various reproductive disorders like dystocia, low conception rate, abortion and retention of placenta. In conclusion, this study provides baseline information on actual production and reproduction potentials of different graded HF×L cows at farmers’ level and the associated limiting factors for crossbred cattle development in Bangladesh. Bangladesh Journal of Animal Science 52 (3): 69-77.

Discrete gradient-zeroing neural network algorithms for handling future quadratic program as well as robot arm via ten-instant formula

The quadratic program, as a fundamental mathematical technique tool, plays a crucial role in applied mathematics and control engineering fields. With the aid of a ten-instant discrete formula processing the precision of order-6, two ten-instant-type discrete gradient-zeroing neural network algorithms that are developed from continuous gradient-zeroing neural network models are proposed to solve the problem of the future quadratic program subject to linear equation constraint with unknown futureness information. The convergence properties of continuous gradient-zeroing neural network models for solving the time-dependent quadratic program problem subject to linear equation constraint are proved by Lyapunov stability theory, while the error pattern properties of ten-instant-type discrete gradient-zeroing neural network algorithms for solving the future quadratic program problem subject to linear equation constraint are studied using the stability theory of the multi-step method. Moreover, two numerical experiments are conducted to show the effectiveness and high precision of the proposed ten-instant-type discrete gradient-zeroing neural network algorithms. In the end, comparison simulations for solving the path-tracking problem of the PUMA560 robot arm are further performed to substantiate the applicability, validity, and superiority of the proposed ten-instant-type discrete gradient-zeroing neural network algorithms.

Learning-Based Multi-Domain Anti-Jamming Communication with Unknown Information

Due to the open nature of the wireless channel, wireless networks are vulnerable to jamming attacks. In this paper, we try to solve the anti-jamming problem caused by smart jammers, which can adaptively adjust the jamming channel and the jamming power. The interaction between the legitimate transmitter and the jammers is modeled as a non-zero-sum game. Considering that it is challenging for the transmitter and the jammers to acquire each other’s information, we propose two anti-jamming communication schemes based on the Deep Q-Network (DQN) algorithm and hierarchical learning (HL) algorithm to solve the non-zero-sum game. Specifically, the DQN-based scheme aims to solve the anti-jamming strategies in the frequency domain and the power domain directly, while the HL-based scheme tries to find the optimal mixed strategies for the Nash equilibrium. Simulation results are presented to validate the effectiveness of the proposed schemes. It is shown that the HL-based scheme has a better convergence performance and the DQN-based scheme has a higher converged utility of the transmitter. In the case of a single jammer, the DQN-based scheme achieves 80% of the transmitter’s utility of the no-jamming case, while the HL-based scheme achieves 63%.

Adaptive Impedance Control for Force Tracking in Manipulators Based on Fractional-Order PID

Force tracking control in robot arms has been widely used in many industrial applications, particularly in tasks involving end effectors and environmental contact, such as grinding, polishing, and other similar operations. However, these environments are not always precisely known. In order to address the force tracking control problem in unknown environments, this paper proposes a fractional-order PID adaptive impedance control strategy based on traditional impedance control. The unknown environmental information is estimated online using the adaptive impedance control algorithm, and the estimated parameters are used to generate reference trajectories to reduce force tracking errors. Fractional-order PID control is then introduced into the system to improve the control performance of the system model, and the theoretical proof of strategy stability is conducted. Finally, a comparison of four strategies was conducted through simulations: traditional impedance control, adaptive hybrid impedance control, adaptive variable impedance control, and the fractional-order PID impedance control proposed in this paper. The simulation results demonstrate that the strategy proposed in this paper exhibits robustness, virtually eliminates overshoot, and enhances response speed. In contrast, both adaptive hybrid impedance control and adaptive variable impedance control exhibit approximately 30% to 45% overshoot during interactions with the environment. Furthermore, in terms of force tracking error, the proposed strategy in this paper outperforms the above two strategies by approximately 29% to 60%, achieving excellent force tracking control performance.

Compensation function observer-based model-compensation backstepping control and application in anti-inference of quadrotor UAV

This study has considered the dynamical equations of a quadrotor unmanned aerial vehicle (UAV) with model uncertainties, time-varying loads and wind disturbances during actual flight. Then, a model-compensated control idea is innovatively proposed for quadrotor UAV attitude and position tracking control, and a compensation function observer-based model-compensated backstepping controller (CFO-based MC-BC) is designed on this basis. The benefits of this approach arc that the proposed controller not only retains the backstepping control strategy in terms of globally asymptotic stability based on Lyapunov criterion. As well as, a compensation function observer (CFO) with high estimation accuracy is used, which can make excellent use of the system state information to accurately estimate the modeling deviations, parameter variations with time, and external unknown disturbances of the system. And it is incorporated into the controller to dynamically offset this negative impact adaptively. This approach effectively addresses the issue of traditional backstepping control performance relying on modeling accuracy. In addition, this paper introduces a high-order differentiator (HOD) that extracts up to the derivatives of the signal with higher accuracy, effectively solving the issue of “differential explosion”. Finally, a large number of simulation results and experimental validation are presented. The results show that the proposed CFO-based MC-BC is superior than the other four schemes in accurate tracking performance, system transient performance, anti-interference ability, anti-time-varying-load, and unknown information estimation ability.

WAVE TRANSFORMATION AND RUNUP VARIABILITY DUE TO WAVE PHASE UNCERTAINTY

Wave runup is a significant portion of total water levels, particularly during storms (Sallenger, 2000). This makes accurate prediction of wave runup paramount. Wave runup can be estimated using simplified empirical models (e.g. Stockdon et al 2006) or simulated using phase-resolved Bousinesq models (Shi et al 2007, Lynett et al 2002) or non-hydrostatic models (e.g. Zijlema et al 2011). These models typically have a variety of offshore boundary input options ranging from spectral to time series, with the most broadly used being spectral parameterizations (e.g. JONSWAP, TMA, etc.) or direct spectral input, both of which neglect phase information at the offshore boundary. Recent research has highlighted the importance of the offshore boundary condition and wave phase at modeling wave runup (e.g. Fiedler et al 2019, Rutten 2021). This work pays careful attention to the consequences of the bound infragravity (IG) wave and explores the influence that unknown phase information can have on predicted wave transformation and resultant wave runup in the context of field observations.

Classification of Malicious URLs Using Naive Bayes and Genetic Algorithm

The financial losses of vulnerable and insecure websites are increasing day by day. The proposed system in this research presents a strategy based on factor analysis of website categories and accurate identification of unknown information to classify safe and dangerous websites and protect users from the previous one. Probability calculations based on Naive Bayes and other powerful approaches are used throughout the website classification procedure to evaluate and train the website classification model. According to our study, the Naive Bayes approach was benign and showed successful results compared to other tests. This strategy is best optimized to solve the problem of distinguishing secure websites from unsafe ones. The vulnerability data categorization training model included in this datasheet had a better degree of precision. In this study, the best accuracy probability of 96% was achieved in Naive Bayes' NSL-KDD data set categorization

Time Reversal Detection for Moving Targets in Clutter Environments

Moving target detection plays a pivotal role in many applications, especially in radar systems. In addition, time-reversal (TR) technology can improve radar detection probability by effectively solving the problem of multipath signals. However, detection performance can be affected when clutter exists and target Doppler shifts are unknown. For this reason, we propose a novel moving target detection algorithm that leverages TR technology and a novel waveform optimization method in clutter and multipath signal environments. In this paper, we establish a TR signal model for a moving target in such environments and derive the TR average likelihood ratio test (TR-ALRT) detector. Additionally, we introduce a waveform optimization method that adapts to clutter and unknown Doppler information further to enhance the performance of the TR-ALRT detector. Consequently, a TR-ALRT waveform optimization (TR-ALRT-WO) detector is derived to adapt to the unknown Doppler and environment information, thereby improving the detection probability. Comparative analysis with other detectors under the same conditions validates the effectiveness of our proposed TR-ALRT-WO detector. Furthermore, numerical experiments demonstrate the superior performance of the proposed detection algorithm.