Design of Water Pollutants Identification System with Spectral-Oriented Sequential Three-Way Decision [Instrumentation and Measurement Systems

Sequential three-way decisions via multi-granularity

In engineering systems design, designers iteratively go back and forth between different design stages to explore the design space and search for the best design solution that satisfies all design constraints. For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are essential to the development of new algorithms embedded with human intelligence to augment the computational design. In this paper, we develop a deep learning-based approach to model and predict designers’ sequential decisions in the systems design context. The core of this approach is an integration of the function-behavior-structure (FBS) model for design process characterization and the long short-term memory unit (LSTM) model for deep leaning. This approach is demonstrated in two case studies on solar energy system design, and its prediction accuracy is evaluated benchmarking on several commonly used models for sequential design decisions, such as the Markov Chain model, the Hidden Markov Chain model, and the random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to rely on both short-term and long-term memory of past design decisions in guiding their future decision-making in the design process. Our approach can support human–computer interactions in design and is general to be applied in other design contexts as long as the sequential data of design actions are available.

During a design process, designers iteratively go back and forth between different design stages to explore the design space and search for the best design solution that satisfies all design constraints. For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are important to the development of new algorithms embedded with human intelligence to augment computational design. In this paper, we develop a deep learning based approach to model and predict designers’ sequential decisions in a system design context. The core of this approach is an integration of the function-behavior-structure model for design process characterization and the long short term memory unit model for deep leaning. This approach is demonstrated in a solar energy system design case study, and its prediction accuracy is evaluated benchmarked on several commonly used models for sequential design decisions, such as Markov Chain model, Hidden Markov Chain model, and random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to reply on both short-term and long-term memory of past design decisions in guiding their decision making in future design process. Our approach is general to be applied in many other design contexts as long as the sequential design action data is available.

Recent years have witnessed an increasing interest in Three-Way Decision (TWD) model. In contrast to traditional two-way decision model, TWD incorporates a boundary decision, which presents a delay-decision choice when available information for a precise decision is insufficient. The boundary decision can be transformed into positive decision or negative decision with the increasing of available information, thus forming a sequential three-way decision process. In real-world decision problems, such sequential three-way decision strategies are frequently used in human decision process. In this paper, we propose a framework of cost-sensitive sequential three-way decision approach to simulate the human decision process in face recognition: a sequential decision process from rough granularity to precise granularity strategy. Both theoretic analysis and experimental verification are presented in this paper.Keywordssequential three-way decisioncost-sensitive learningface recognitiondecision-theoretic rough sets

The traditional temperature measurement and control system of chemical instruments can not accurately grasp the basic condition of temperature data, resulting in low efficiency of measurement and control. Therefore, a temperature measurement and control system of chemical instruments based on the Internet of things is designed. According to the relevant performance of the hardware components of the system, the system information of the control center is studied, and the correlation of the internal system is studied. Based on this, the command of hardware mode transformation is executed. After the hardware design is realized, the system software design is realized on the premise of hardware data. Combined with the measurement and control algorithm, the measurement and control mode of the center is continuously studied, the data difference between the systems is adjusted, and the contradiction between the measurement and control data is avoided. The experimental results show that the design of chemical instrument temperature measurement and control system based on Internet of things has higher measurement and control efficiency, shorter measurement and control time, and improves the accuracy of measurement and control.KeywordsInternet of thingsTemperature measurement and control of chemical instrumentsTemperature measurement and control systemDesign of measurement and control system

Research on multi-granularity sequential three-way decisions based on the fuzzy T-equivalence relation

A multilevel neighborhood sequential decision approach of three-way granular computing

The paper describes a control and measurement system for controlling and analysing operation of a stepper motor. The system design is based on the concept of virtual instrument, where data acquisition and transmission use standard solutions, whereas software serves both as a generator and a measurement system – an oscilloscope. The generator allows efficient control of motor operation in the following control modes: wave, full-step and half-step control. There were no winding powering sequence errors observed. The measurement system allows presenting and analysing voltage waveforms in individual windings with possibility to detect couplings and overvoltages. It was also shown that it is possible to detect locking of rotor rotation.The proposed virtual instruments have open structure, which enables extended analysis based on available measurements or expand range of measurements as long as measurement and control system instrumentation allows that.

The existing three-way decision-making methods are classified into two classes-a single one-step three-way decision-making and a sequential, multi-step three-way decision-making, and this paper studies the latter. In multi-granularity model, sequential thresholds are used to represent its multi-level granularity. Firstly, the concept of similarity is introduced and tolerance-based fuzzy decision theory rough set is proposed to obtain the upper and lower approximation of decision class and the three-way decisions of the whole decision class. Secondly, the tolerance-based sequential three-way decision is extended to the case of multi-granularity, and the concepts of upper and lower approximation and its three-way decision in optimistic and pessimistic situations are proposed, and some related properties are verified. By adopting the aggressive and conservative strategy, we put forward the concepts of upper and lower approximation and related properties under pessimistic-optimistic conditions as well as sequential three-way decision, the validity of these methods is verified by an example. In optimistic-pessimistic situations, a counterexample is given to prove that it is unable to make decisions. On this basis, uncertainty measures, precision and roughness, are introduced, and some properties of them are studied. Finally, the paper analyses and proves the relationship between the above three models.

Attribute reduction for sequential three-way decisions under dynamic granulation

Sequential three-way decision and granulation for cost-sensitive face recognition

Multigranulation sequential three-way decisions based on multiple thresholds

VIKOR uses the idea of overall utility maximization and individual regret minimization to afford a compromise result for multi-attribute decision-making problems with conflicting attributes. Many researchers have proposed corresponding improvements and expansions to make it more suitable for sorting optimization in their respective research fields. However, these improvements and extensions only rank the alternatives without classifying them. For this purpose, this text introduces the three-way sequential decisions method and combines it with the VIKOR method to design a three-way VIKOR method that can deal with both ranking and classification. By using the final negative ideal solution (NIS) and the final positive ideal solution (PIS) for all alternatives, the individual regret value and group utility value of each alternative were calculated. Different three-way VIKOR models were obtained by four different combinations of individual regret value and group utility value. In the ranking process, the characteristics of VIKOR method are introduced, and the subjective preference of decision makers is considered by using individual regret, group utility, and decision index values. In the classification process, the corresponding alternatives are divided into the corresponding decision domains by sequential three-way decisions, and the risk of direct acceptance or rejection is avoided by putting the uncertain alternatives into the boundary region to delay the decision. The alternative is divided into decision domains through sequential three-way decisions, sorted according to the collation rules in the same decision domain, and the final sorting results are obtained according to the collation rules in different decision domains. Finally, the effectiveness and correctness of the proposed method are verified by a project investment example, and the results are compared and evaluated. The experimental results show that the proposed method has a significant correlation with the results of other methods, ad is effective and feasible, and is simpler and more effective in dealing with some problems. Errors caused by misclassification is reduced by sequential three-way decisions.

As an effective method for uncertain knowledge discovery and decision-making, the three-way decisions model has attracted extensive attention from scholars. However, in practice, the existing sequential three-way decision model often faces challenges due to factors such as missing data and unbalanced attribute granularity. To address these issues, we propose an intuitionistic fuzzy sequential three-way decision (IFSTWD) model, which introduces several significant contributions: (1) New intuitionistic fuzzy similarity relations. By integrating possibility theory, our model defines similarity and dissimilarity in incomplete information systems, establishing new intuitionistic fuzzy similarity relations and their cut relations. (2) Granulation method innovation. We propose a density neighborhood-based granulation method to partition decision attributes and introduce a novel criterion for evaluating attribute importance. (3) Enhanced decision process. By incorporating sequential three-way decision theory and developing a multi-level granularity structure, our model replaces the traditional equivalent relation in the decision-theoretic rough sets model, thus advancing the model’s applicability and effectiveness. The practical utility of our model is demonstrated through an example analysis of “Chinese + vocational skills” talent competency and validated through simulation experiments on the UCI dataset, showing superior performance compared to existing methods.

The design of measurement and control system of the radon detection instrument, which used for geological prospecting and environmental monitoring, is introduced in this paper. This paper includes the present research situation of the radon detection instrument, the software and hardware design, the focus is the data processing software design which based on the fourth language Delphi. The data processing software includes the data serial communication, the data acceptance, the data display, the data storage, the curve drawing, the database management and so on.