Feedback Adjustment Research Articles

Development of an improved mass transfer model for condensation with dynamic feedback regulation

In the field of condensation research, accurate and efficient numerical simulation models are highly desired. One of the widely used phase change mass transfer models is known as the Lee model. However, this model has limitations due to uncertainties in the mass transfer intensity factor, which can lead to simulation failures and impede large-scale engineering applications. This study presents an improved mass transfer model which employs a tuning function to dynamically and accurately modify the mass transfer intensity factor in each two-phase cell via feedback adjustment. This approach enables immediate and customized adjustment of the factor for each grid within the limit of the tuning function, significantly reducing the errors caused by repeated artificial adjustments and related uncertainties. The adjustment capabilities of the hyperbolic tangent and softsign functions are evaluated. The softsign function is superior in both regulation efficiency and accuracy. The model's accuracy is verified by comparing with previous experiments and simulations, as well as by the one-dimensional Stefan problem. The proposed model achieves high prediction accuracy, reduces the risk of simulation dispersion, and significantly enhances computational efficiency with the iteration number reduced by nearly half under certain conditions compared to the Lee model. Furthermore, the new model shows robustness as the prediction accuracy is insensitive to variations in critical parameters. The improved model is expected to provide valuable assistance for future research and applications in flow condensation with outstanding computational accuracy, efficiency, and stability.

Minimum cost consensus model with variable cost for multi-criteria large-scale group decision-making considering the tolerance of decision-makers

The objective of multicriteria large-scale group decision-making (LSGDM) is to obtain a decision result that gains acceptance from the majority of decision-makers (DMs). The consensus reaching process (CRP) plays a crucial role in guiding DMs to continuously adjust their opinions through feedback adjustment mechanisms to form a widely accepted decision result. In practical decision-making, adjusting opinions incurs costs and involves limited resources. Simultaneously, DMs have tolerance for modifying their opinions. To this end, this paper proposes a minimum cost consensus model (MCCM) with variable cost to reach consensus efficiently within limited resources and DMs’ tolerance. Firstly, a method to measure the combined similarity among DMs is proposed. Then, an extended local fitness maximization (ELFM) algorithm suitable for multicriteria LSGDM problems is proposed for clustering. Furthermore, an objective method of determining the unit adjustment costs for DMs in the MCCM is provided. For the CRP that considers the tolerance of DMs, this paper introduces the MCCM with variable cost to obtain the adjustment opinions of DMs. During the selection process, we utilize the stochastic multicriteria acceptability analysis (SMAA) method to evaluate alternatives and selection. Finally, the feasibility of the proposed method is verified through an illustrative example, and its advantages are emphasized through comparative analysis.

A dual-level consensus model for large-scale group decision-making driven by trust relationships in social networks

In large-scale group decision-making, obtaining reasonable and reliable decision results is the research focus. To this end, a dual-level consensus model for large-scale group decision-making is constructed. In the model, we first presented a method for measuring the two-dimensional similarity between decision-makers. The method considered the opinion similarity at evaluation scales and ranking results level, which provided a more comprehensive measure of the opinion differences between decision-makers. A hierarchical clustering algorithm based on the two-dimensional similarity-trust matrix is proposed. By considering the two-dimensional similarity and trust relationships among decision-makers, the method contributed to the formation of subgroups with excellent cooperative and communicative atmospheres. Furthermore, we introduced a dual-level consensus measures method to comprehensively and accurately assess the group consensus level. To increase the efficiency of decision-making and the satisfaction of decision-makers, we proposed a personalized feedback adjustment mechanism for decision-makers. Additionally, for the subgroups that have not reached consensus, a social networks DeGroot model is constructed to adjust the opinion of subgroups. Finally, a case study of low-carbon supplier selection verified the feasibility and applicability of the model. The effectiveness and superiority of the model are demonstrated by comparative and simulation analysis. The analysis results show that the model is conducive to promoting cooperation and coordination among decision-makers and obtaining highly acceptable and quality decision results.

Multi-strategy Improved Pelican Optimization Algorithm for Mobile Robot Path Planning

In response to the problems of easily falling into local optima, low path planning accuracy, and slow convergence speed when applying the traditional pelican optimization algorithm to the mobile robot path planning problem, a multi-strategy improved pelican optimization algorithm (MPOA) is proposed. In the initialization stage, chaotic mapping is used to increase the diversity of the pelican population individuals. In the exploration stage, an adaptive feedback adjustment factor is proposed to adjust the local optima of pelican individuals’ positions and balance the algorithm’s local development capability. In the development stage, the Lévy flight strategy is introduced to adjust the domain radius of the pelican population individuals, and the Gaussian mutation mechanism is used to disturb individuals that have fallen into local optima. Simulation experimental results show that the improved algorithm has significantly improved and effectively shortened the length of the planned path.

Chaotic image encryption algorithm based on bit-level feedback adjustment

This paper proposes a fast bit-level chaotic image encryption algorithm. The goal is to make the difference between 0 and 1 in the bit-level ciphertext as small as possible. By introducing the concept of the difference between 0 and 1, constantly adjust the number of binary 0 and 1 in the ciphertext, and the difference is fed back to the encryption system until the difference reaches the preset accuracy, so as to quickly achieve a secure encryption effect. To verify the security of the algorithm, this paper selects multiple chaotic systems for testing and applies different chaotic systems to the encryption algorithm. Moreover, the proposed algorithm is compared with the bit-level and non-bit-level image encryption algorithms proposed by other scholars in recent years, and the results show that the proposed algorithm has higher security than other algorithms.

A Consensus-Based 360 Degree Feedback Evaluation Method with Linguistic Distribution Assessments

The 360 degree feedback evaluation method is a multidimensional, comprehensive assessment method. Evaluators may hesitate among multiple evaluation values and be simultaneously constrained by the biases and cognitive errors of the evaluators, evaluation results are prone to unfairness and conflicts. To overcome these issues, this paper proposes a consensus-based 360 degree feedback evaluation method with linguistic distribution assessments. Firstly, evaluators provide evaluation information in the form of linguistic distribution. Secondly, utilizing an enhanced ordered weighted averaging (OWA) operator, the model aggregates multi-source evaluation information to handle biased evaluation information effectively. Subsequently, a consensus-reaching process is established to coordinate conflicting viewpoints among the evaluators, and a feedback adjustment mechanism is designed to guide evaluators in refining their evaluation information, facilitating the attainment of a unanimous evaluation outcome. Finally, the improved 360 degree feedback evaluation method was applied to the performance evaluation of the project leaders in company J, thereby validating the effectiveness and rationality of the method.

Considering personalized individual semantics with ordinal and cardinal consensus reaching processes via three-way decision and regret theory

The seamless integration of computerized methodologies into industrial engineering problem-solving is pivotal for optimizing efficiency. In the specific domain of multi-attribute group decision-making (MAGDM) with probabilistic linguistic term sets (PLTSs), these methodologies offer systematic approaches to consensus building, ensuring effective decision processes in intricate scenarios. Within the realm of PLTSs, the consensus-reaching process (CRP) for MAGDM is gaining prominence. This paper addresses this evolving area by proposing ordinal and cardinal CRPs within the framework of PLTSs, specifically incorporating the regret theory (RT) of three-way decisions (TWD). The paper introduces an initial distance formula under PLTSs, providing a complementary approach to assess similarity relations among decision-makers (DMs). To account for diverse semantics across DMs, personalized individual semantics (PIS) is integrated into the CRP, recognizing variations in DMs’ alternatives and attributes. To enhance realism, the paper introduces the concepts of individual alternative sets and individual attribute sets. Additionally, the paper integrates ordinal and cardinal consensus, establishing a dynamic feedback adjustment mechanism grounded in the principles of RT and TWD. The method’s reasonableness is validated through a real case study, and a comparative analysis with the existing methods underscores the superiority of the approach presented in this paper.

The three axioms of resilience.

This article summarizes the growing literature on resilience in the face of aversive and potentially traumatic events (PTEs) in three basic axioms: (a) resilience is a common outcome, 2) there are no "key" traits to resilience (resilience is not a type), and (c) resilient outcomes occur through flexible self-regulation. The first axiom highlights the limitation of a traditional, binary view of trauma in terms of the presence-absence of posttraumatic stress disorder, emphasizing instead the heterogeneity of trauma outcomes. Four prototypical trajectories are reviewed: chronic symptoms; delayed symptoms; recovery; and the most common trajectory, resilience. The second axiom reviews the paradoxical inability of known correlates of resilience to adequately predict resilient outcomes. Resilience is instead described as a multifaceted phenomenon with no singular set of key traits, as various factors contribute to resilient outcomes in complex ways. The third axiom provides an explanation of how resilience can emerge from this complex array by introducing the concept of flexible self-regulation. The flexibility mindset is described as providing the motivation for flexible responding. The flexibility sequence is described as explaining the mechanisms underlying flexibility in terms of three serial stages: context sensitivity, strategy repertoire, and feedback adjustment. The final section briefly considers clinical implications.

Symmetric Collaborative Fault-Tolerant Control of Multi-Intelligence under Long-Range Transmission in Air–Ground Integrated Wireless High-Mobility Self-Organizing Networks

With the continuous development and progress of wireless self-organizing network communication technology, how to carry out long-distance cooperative control of multiple intelligences under the framework of an air–ground integrated wireless high-mobility self-organizing network has become a hot and difficult topic that needs to be solved urgently. This paper takes the air–ground integrated wireless high-mobility self-organizing network system as the basic framework and focuses on solving the long-distance cooperative fault-tolerant control of multi-intelligent bodies and the topological stability of a wireless mobile self-organizing network. To solve the above problems, a direct neural network with a robust adaptive fault-tolerant controller is designed in this paper. By constructing a symmetric population neural network model and combining it with the Lyapunov stabilization criterion, the system feedback matrix K has the ability of autonomous adaptive learning, and symmetrically distorts, rotates, or scales the training data to instantly adjust the system’s fault-tolerant corrections and adaptive adjusting factors to resist the unknown disturbances and faults, to achieve the goals of multi-intelligent body stable control and the stable operation of a wireless high-mobility self-organizing network topology. Simulation results show that with the feedback adjustment of the multi-system under the designed controller, the multi-system as a whole has good fault-tolerant performance and autonomous learning approximation performance, and the tracking error asymptotically converges to zero. The experimental results show that the multi-flight subsystems fly stably, the air–ground integrated wireless high-mobility self-organizing network topology has good stability performance, and the maximum relative improvement of the topology stability performance is 50%.

Modified LDA vector and feedback analysis for short query Information Retrieval systems

Abstract Information Retrieval systems benefit from the use of long queries containing a large volume of search-relevant information. This situation is not common, as users of such systems tend to use very short and precise queries with few keywords. In this work we propose a modification of the Latent Dirichlet Allocation (LDA) technique using data from the document collection and its vocabulary for a better representation of short queries. Additionally, a study is carried out on how the modification of the proposed LDA weighted vectors increase the performance using relevant documents as feedback. The work shown in this paper is tested using three biomedical corpora (TREC Genomics 2004, TREC Genomics 2005 and OHSUMED) and one legal corpus (FIRE 2017). Results prove that the application of the proposed representation technique, as well as the feedback adjustment, clearly outperforms the baseline methods (BM25 and non-modified LDA).

Continual Monitoring of Respiratory Disorders to Enhance Therapy via Real-Time Lung Sound Imaging in Telemedicine

This work presents a configurable Internet of Things architecture for acoustical sensing and analysis for frequent remote respiratory assessments. The proposed system creates a foundation for enabling real-time therapy and patient feedback adjustment in a telemedicine setting. By allowing continuous remote respiratory monitoring, the system has the potential to give clinicians access to assessments from which they could make decisions about modifying therapy in real-time and communicate changes directly to patients. The system comprises a wearable wireless microphone array interfaced with a programmable microcontroller with embedded signal conditioning. Experiments on the phantom model were conducted to demonstrate the feasibility of reconstructing acoustic lung images for detecting obstructions in the airway and provided controlled validation of noise resilience and imaging capabilities. An optimized denoising technique and design innovations provided 7 dB more SNR and 7% more imaging accuracy for the proposed system, benchmarked against digital stethoscopes. While further clinical studies are warranted, initial results suggest potential benefits over single-point digital stethoscopes for internet-enabled remote lung monitoring needing noise immunity and regional specificity. The flexible architecture aims to bridge critical technical gaps in frequent and connected respiratory function at home or in busy clinical settings challenged by ambient noise interference.

Dynamic responses of the floatover system with nonstationary mating processes: An experimental study

Floatover method has been widely applied in the installation of both fixed and floating platforms, which has the advantages of high efficiency, economy, and large lifting capacity compared with other installation methods. Few studies could numerically and experimentally simulate the continuous load transfer process which shows significant nonstationary characteristics. This paper conducts experimental research on the nonstationary mating process of the floatover system. The ballasting system based on the control logic of feedback adjustment is developed to adjust the draft and inertia properties of the floatover vessel, and the validation tests are conducted to verify the feasibility and reliability of the system. The load buffer devices are designed carefully and the piecewise linear spring simulation method is applied to simulate the nonlinear stiffnesses of the elastic cushion. The stationary model for 0% and 100% load transfer stages and the nonstationary model for continuous load transfer process are established and tested in the model tests, and the dynamic responses are analyzed by using statistical and spectral methods respectively. Stationary model tests indicate that the spectral characteristics and distribution of LMU (Leg Mating Unit) dynamic loads in transverse symmetric positions are similar, and the statistical values are also relatively close under different wave heads. The nonstationary dynamic responses are segmented according to the physical process, time-frequency spectrum and piecewise statistics diagram to find the critical stage. The greatest dynamic load occurs in the intermediate stage of the mating process when the larger stiffness of the LMU acts and the steel-to-steel contact will also occur.

An adaptive consensus model for multi-criteria sorting under linguistic distribution group decision making considering decision-makers’ attitudes

Group multiple criteria sorting (MCS) has become a trend in dealing with a variety of practical problems. During the process of managing group MCS, it is critical to reduce conflicts among decision-makers (DMs). Given the key role of DMs’ attitudes in affecting consensus level, this study aims to propose a novel consensus-based approach to solve group MCS problems considering DMs’ attitudes with flexible expression linguistic distribution assessments (LDAs) that can capture massive DMs’ qualitative preferences. To achieve this goal, first, a minimum adjustment-based optimization model is built to guide individuals in revising their preferences, and a maximum assignment interval-based optimization model is constructed to derive consistent and possible assignments of each alternative while maintaining the accuracy levels of the original assignments. An attitudinal consensus index is then defined to measure the group consensus level, by which group DMs’ attitudes can be well considered in MCS problems. A sophisticated adaptive feedback adjustment mechanism is also developed and inserted into the consensus model, which provides support for consensus-reaching based on the advantages of both types of adaptive feedback adjustment mechanism strategies. Afterwards, to generate more straightforward and scientific assignment solutions, this study proposes a minimum information loss-based optimization model to identify the final categories of each alternative. Finally, an illustrative example for evaluating livable cities, followed by sensitivity and comparative analyses, is presented to demonstrate the applicability and advantages of the proposed MCS approach.

New distance measure-driven flexible linguistic consensus model with application to urban flooding risk assessment

As a novel linguistic representation tool, flexible linguistic expression (FLE) has substantial flexibility in expressing ambiguous and uncertain information from experts. However, FLEs pose the following challenges to developing information measurement and consensus mechanisms for FLEs-based multi-attribute group decision-making (MAGDM). (1) The current studies primarily use linguistic distribution approximation or triangular fuzzy numbers to measure the difference between FLEs indirectly without proposing an exact distance measure. (2) Few studies have been conducted on direct accurate consensus models incorporating adaptive consensus feedback mechanisms. To deal with these issues, the flexible linguistic ordinal Deng entropy (FLODE) is firstly proposed to measure the uncertainty of FLEs, and by combining it with linguistic scale functions, a new distance measure for FLEs is constructed, which can accurately measure the level of group consensus and the difference in pairwise FLEs. Subsequently, by integrating the defined FLODE with the classical entropy weight method, a new method for determining the experts’ personalized attribute weights is constructed, fully considering the impact of experts’ personalized attribute weights on decision-making results. Also, a new consensus model incorporating a minimum adjustment feedback mechanism and dynamic personalized attribute weights is developed, in which the feedback adjustment coefficients and personalized attribute weights are dynamically updated during the consensus reaching process (CRP). Lastly, an urban flooding risk assessment is applied to confirm the effectiveness of the proposed consensus model.

Influence measure-based large-scale group decision making with linear uncertain preference relations

This research focuses on the problem of large-scale group decision-making (LSGDM) based on influence measure under linear uncertain preferences. The value of this research is that it improves the performance of the current clustering algorithms, increases the efficiency of consensus reaching for major decision-making events, thus reducing the cost of feedback adjustments, and at the same time reflecting the risk attitude of the decision-makers (DMs) during situations of uncertainty. First, an approach for measuring the influence of the DMs is presented. Based on the influence measure, a clustering method is proposed to classify the experts into subgroups. Then, a method for determining the weights of the subgroups is developed by combining the influence and credibility of the subgroup leader as well as the intra-subgroup consensus level. Subsequently, a feedback adjustment method is provided to reduce the disagreement among the DMs by considering the unit adjustment cost and the corresponding adjustment willingness of the DMs. Further, based on the trust risk and credibility of the DMs, a dual management mechanism of non-cooperative behavior is established to hasten consensus. Finally, a case study is presented to demonstrate the practicality of the suggested approach, while simulation experiments and comparative analysis are conducted to verify the model.

Design and experiment of high-speed and precise positioning seeding control system for rice seedlings based on dual-position feedback adjustment (DPFA)

To improve the alignment accuracy of the mechanical drum-type rice bowl seedling raising precision seeding line under the high-speed sowing operation state between the seed outlet and the bowl hole. This paper proposes a high-speed and precise positioning seeding control system for rice seedlings based on dual position feedback adjustment (DPFA). Based on Siemens PLC controller, through sensor selection and installation, controller parameter acquisition and calculation, and human–computer interaction software development. We have completed the design of the high-speed and precise alignment seeding control system for rice pot seedlings. Realize the dual position detection of the mechanical drum-type seed wheel and the 406-hole tray. Applying the fuzzy PID controller optimized the dual-position feedback regulation rice pot seedling high-speed and precise alignment planting control system. The response time was shortened to 0.0432 s, the overshoot was 4.5 %, and the adjustment time was 0.1105 s. The sowing test was conducted with sowing speed and height as the main influencing factors and sowing uniformity, leakage rate, and sowing pass rate as the evaluation indexes. The test results show that the sowing speed is within 500 trays/h ∼ 600 trays/h, the sowing uniformity is higher than 65 %, the missed seeding rate is less than 1.5 %, and the qualified rate of sowing is above 94 %. Among them, the maximum sowing uniformity was 66.27 %, the minimum value of missing seeding was 0.89 %, and the sowing qualification rate was 97.06 %. The accuracy rate of material shortage alarms is 100 %. The DPFA-based high-speed and precise alignment sowing control system for rice pot seedlings can realize the exact alignment control function of the mechanical drum-type seedling raising and sowing line.

Application and Practice of Intelligent Algorithms in Computer Software Design and Testing

With the rapid development of information technology, software has become a part of social life. However, for complex software systems, traditional design and testing methods face a series of problems such as low testing coverage and low efficiency. The main purpose of this article is to study the optimization of software design and testing processes using intelligent algorithms, in order to improve the quality and efficiency of software. This article established a real-time optimization and feedback adjustment mechanism during the software design and testing phase, enabling intelligent algorithms to respond promptly to the environment and requirements. Monitoring and control strategies were designed to ensure the algorithm runs stably for a long time. Test method 1 (traditional method) had 100 test cases, a total execution time of 1200 seconds, and an average execution time of 12 seconds per test case; test method 2 (Intelligent Algorithm Optimization) had 100 test cases, a total execution time of 800 seconds, and an average execution time of 8 seconds per test case. The research results of this article play a good promoting role in improving software testing coverage and software structure optimization, and also provide new ideas and methods for the promotion of intelligent algorithms in software engineering.

Research on global optimization mechanism of intelligent compaction parameters of soil subgrade based on difference method

The global optimization mechanism plays a crucial role in enhancing construction efficiency and ensuring construction quality of soil subgrade such as building foundation or roadbed, within the context of intelligent compaction technology. There are three parts in the global optimization mechanism, which are determining the optimal work parameters, judging the compaction quality, and the feedback adjustment method. To establish a global optimization mechanism for intelligent compaction of subgrade soil, this paper developed a numerical simulation model for subgrade intelligent compaction, defined the necessary numerical simulation conditions for the global optimization mechanism of intelligent compaction. In this way, it provides a source of data for the establishment of global optimization mechanisms. Furthermore, the optimal working parameters are determined by the method of multivariate nonlinear fitting and calculating its extreme value. Meanwhile, an intelligent compaction quality judgment mechanism is established. Additionally, a feedback adjustment method for intelligent compaction was developed using the difference method to form a comprehensive global optimization mechanism. Finally, the intelligent compaction's global optimization mechanism has been validated through field experiments. The results of this study demonstrate that the boundary between low speed and medium speed rolling is 1.7 m/s. The optimal number of rolling passes is four and the ideal rolling speed ranges from 1.2 to 1.3 m/s when initial compaction degree reaches between 80 % and 90 %. The compaction degree, CMV (Compaction Meter Value) and AICV (Acceleration Intelligent Compaction Value) all exhibit a decreasing trend of subgrade compaction quality with increasing rolling speed, and the rate of decrease gradually accelerates.

Research on Low Noise Chopping Amplifier Circuit Based on Feedback Regulation

In order to overcome the limitation of 1/f noise on long-period magnetotelluric sensors, it is necessary to use low-noise chopping technology for signal conditioning. However, the static operating point drift of Junction Field-Effect Transistor (JFET) is difficult to achieve stable amplification with the chopper technology. This paper proposes a low noise amplifier (LNA) with chopper technology based on feedback adjustment. The LNA reduces the turning frequency of the circuit, and maintains the JFET static point. At 375mHz, its noise is 2.55nV/Hz .

Managing non-cooperative behaviors and ordinal consensus through a self-organized mechanism in multi-attribute group decision making

In consensus-based multi-attribute group decision making (MAGDM) problems, decision makers (DMs) may exhibit non-cooperative behaviors since they usually have different individual interests (sometimes conflicting) or limited knowledge, which strongly affects the efficiency of consensus and the quality of decision outcomes. Additionally, the existing MAGDM studies mainly focus on the cardinal consensus, and the ordinal consensus is ignored. Thus, this paper proposes a self-organized mechanism based framework to manage non-cooperative behaviors and ordinal consensus in MAGDM. First, a dual-membership function based on the basic idea of synergy theory is designed to detect non-cooperative behaviors at the element level of the multiple attribute evaluation matrix (MAEM), and then the weights of elements with non-cooperative behaviors are penalized automatically. In this way, the negative effects of non-cooperative behaviors can be eliminated. Next, a novel preference ranking-based ordinal consensus approach is proposed, which calculates an ordinal consensus based on the preference rankings of alternatives between individuals and the group. If the pre-defined consensus level is not reached, the feedback adjustment is used to help DMs modify their MAEMs to improve the consensus level; otherwise, the selection process is utilized to choose the optimal alternative(s). Finally, detailed simulation experiments and comparative analysis are designed to show the properties and effectiveness of the proposed framework, and an illustrative angel investment case is presented to demonstrate the calculation process and usability.