# Multisensor Cross-Media Data Mining Method Assisted by Expert System

Authors: Xin Shan
Journal: Journal of Sensors
 (2023)

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## Abstract

The information generated by the multifaceted and multilevel processing of the data of multiple sensors is more meaningful than the information obtained by a single sensor and provides accurate information and decision-making basis for various application systems. How to apply data mining theory to the multisensor cross-media field has become a research hotspot. Through the analysis of multisensor cross-media data, it is of great significance to dig out the important rules, information, or knowledge hidden in it and use it for cross-media retrieval engine. The rule acquisition is the “bottleneck” problem of the expert system. This paper adopts the data mining method based on the rough set to acquire the rules and improves the basic algorithm of attribute reduction. Using the attribute reduction algorithm and the heuristic value reduction algorithm, the calculation is simplified and the reduction efficiency is improved. In the presentation, according to the characteristics of cross-media and the application requirements of expert systems, this paper takes the case representation based on features as the basis and classifies cases according to feature attributes. In case retrieval, according to the hierarchical structure of case features, the entire case database is organized into a multilevel hierarchical index structure. In this paper, a cross-media retrieval engine is constructed from the perspective of classifier design, and the Euclidean distance is used as the similarity matching model of image content. The mutual retrieval of images and audios preliminarily forms the design process of retrieval from one media type to another and establishes a corresponding cross-media index. The experimental results show that the algorithm has better processing effect and higher accuracy than other algorithms. Different k-nearest neighbor values were selected in the experiment, and it reached about 96% in the test environment of libsvm toolbox, which is better than the processing results of LE and LLE algorithms.


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## Body

## 1. Introduction

Multisensor information fusion is a method of summarizing, combining, and comprehensively analyzing local information from multiple sources and multidimensionally processing the inconsistencies and uncertainties between the information collected by multiple sensors to achieve comprehensive information processing [1]. It is a multilevel system integrating detection, correlation, estimation, filtering, prediction, and identification. Multisensor information fusion systems are now widely used in various fields, such as military fields, sensor networks, robotics, video and image processing, and intelligent system design [2]. In a single-sensor system, limited by the detection range, detection accuracy, and noise or interference that may be encountered in the measurement, the information detected by a single sensor is often inaccurate and incomplete [3]. The detection task requires multiple sensors to work together, so the single-sensor system has great limitations. In a multisensor system, multiple sensors detect a target at the same time, and there is redundancy and complementarity between the detected data. The final fusion result does not depend on a certain sensor, even if there is a problem with one sensor. The detection information of other effective sensors can also be used for fusion, so multisensors can effectively improve the reliability and stability of the fusion system [4]. In multisensor information fusion, many key technologies are involved, such as data association, track filtering, and target recognition. There are still some difficulties to be solved in these fields [5].With the innovation of science and technology and the improvement of industrial production capacity, the cost of computer popularization as the carrier of the Internet has gradually decreased. In recent years, the concept of Internet + has been put forward. As an emerging information communication medium, the Internet has gradually replaced the traditional information media such as newspapers and magazines and has gradually become the main channel for people to obtain information on a daily basis [6]. Due to the limitation of the medium, the capacity of a single text information carrier was extremely limited in the past, but the Internet, with its powerful data circulation capability, has spawned more intuitive and specific multimedia information carriers, enabling users to obtain more comprehensive information [7]. However, the explosive growth of the amount of data on the Internet and the diversification of multimedia carriers make people no longer satisfied with the retrieval in a single multimedia form. Without knowing the singer’s name, the user wants to search for the singer’s photo album, and the audio retrieval alone cannot meet the needs. At this time, it is necessary to retrieve the image through audio across the media. At the same time, scientific experiments have shown that when people are exposed to new things, receiving different multimedia information such as audio and images at the same time will make people understand new things more comprehensively and leave a deeper impression [8]. It can be seen that cross-media retrieval not only has practical application value in real life but also has theoretical research significance in the field of science.In this paper, the theoretical basis of rule-based reasoning diagnosis method is discussed, and the method of obtaining and representing relational knowledge is studied, and the data mining method based on rough set theory is proposed to solve the problem of obtaining rules. Specifically, the technical contributions of this paper can be summarized as follows:First, this paper uses the attribute reduction algorithm in rough set to filter redundant attributes. After attribute value reduction, it establishes the decision table of multisensor cross-media data mining, deduces the rules, and forms the knowledge base, which provides the basis for multisensor cross-media data mining.Second, this paper introduces the production rule knowledge expression method, using the production rule form to represent the rule knowledge. In this paper, a unified description and fusion method for multisource heterogeneous information in condition monitoring, and multisensor cross-media data mining is proposed using random set theory. The method of determining the observed value of global sensor by the observation of each independent sensor is given. The value of the global sensor is matched with each state in the sample database, and the likelihood function is used to determine the basic probability allocation value which can be used for random set fusion.Third, this paper deals with the subjective fuzzy information of expert opinions by using the theory of random sets and then gives the basic probability assignment method of subjective fuzzy information. On this basis, the synthetic formula under random set theory is used to fuse the information of two different data sources, and the final state detection result is given.Fourth, this paper introduces a model-the-cross-media primitive generation model and the Laplacian eigenmap based on adaptive nearest neighbor. Some experiments are carried out on this model to realize the index establishment of cross-media retrieval, and the process of cross-media retrieval is analyzed, including the use of improved DTW algorithm based on speech, SVD algorithm based on image, and MDS algorithm. It is verified that this model is effective in practice from different perspectives. Through comparative experiments, we analyze the good characteristics of the nearest neighbor matching algorithm and give the visualization results of these simulations.


## 2. Related Work

Although the application research of information fusion has been so extensive, the information fusion itself has not yet formed a basic theoretical framework and an effective generalized fusion model and algorithm [9]. Most of its work is to carry out research on problems in specific application fields; that is to say, the current research on information fusion problems is based on the types of problems. These studies only reflect the inherent object-oriented characteristics of information fusion, and it is difficult to form a complete theoretical system necessary for information fusion as an independent discipline [10]. This lack of theory hinders researchers’ in-depth understanding of information fusion itself and also makes information fusion only regarded as a concept of multisensor information processing to some extent; it is difficult for people to comprehensively analyze and evaluate fusion systems, which makes the design of the fusion system with certain blindness. Therefore, even though many mathematicians have explored the performance evaluation of fusion systems, most of these evaluations only propose some specific system performance indicators, or analyze a certain fusion algorithm for a specific application background [11].In the field of information fusion, theoretical research is far behind the actual needs, and so far, there has not been a relatively strict principle and method with a general guiding significance [12]. The multisource information fusion technology is still a very immature technology. At present, there is no unified definition of the function and form of the information fusion process itself, and a general mathematical model cannot be established for general information fusion. Most of them are based on the expert system design method, and the basic research on the general structure design specially used for the multisensor information fusion problem has been started, but there is no breakthrough substantial progress. There are various opinions on the research content and methods of information fusion [13]. Some people interpret information fusion as the optimal estimation of multi-information from the perspective of mathematics and control, while others think that information fusion cannot simply be regarded as a thing or a technology but should be regarded as an intelligent way of thinking. Although opinions vary, there is a common understanding that, in general, more accurate, more comprehensive, and more reliable environmental information can be obtained using multiple sources of information than only a single source of information [14].Topic models are widely used in cross-media-related problems. The earliest corresponding implicit Dirichlet distribution proposed to effectively model the joint distribution of the two modalities and the conditional distribution of the text given the corresponding image, taking a set of latent topics as latent variables and dividing the two modalities [15], whereas in the multimodal implicit Dirichlet distribution model for topic regression, two independent sets of semantic topics are learned and linked by the regression model. The multimodal document random field model describes the relationship between documents by defining a Markov random field [16].The early multimedia retrieval technology was based on text, usually manual keyword annotation was performed on multimedia resources, and then, the resources with the highest similarity were found by keyword matching during retrieval [17]. This method was effective in the early days when the flow of information was small. However, with the geometric multiple growth of the amount of multimedia information on the Internet, this method has begun to appear somewhat ineffective. It not only requires a lot of time and labor costs but also requires manual annotation [18]. Often with a certain subjectivity, it is difficult to completely and objectively describe the accurate information of the multimedia resource itself, and it is easy to cause additional adverse effects during retrieval, and for multimedia forms containing a large amount of information such as audio or video, manual processing is even more difficult [19]. Considering the limitations of keyword-based retrieval, some scholars focus on the content information contained in multimedia resources and propose content-based multimedia retrieval technology [20]. This method abandons the subjectivity of manual annotation processing and uses a specific feature extraction method to extract the underlying features of the data to represent multimedia information. During retrieval, the similarity between the features can be used to measure the matching degree of the target.


## 3. Methods

### 3.1. The Working Model of Rule Inference

The diagnosis technology based on rule-based reasoning has the advantages of clear logic, good interpretability, and low misdiagnosis rate and plays an important role in multisensor cross-media data mining analysis.RBR diagnosis technology is mainly based on the change of system parameters and whether it exceeds the limit value to judge the system state and identify the contact type. When diagnosing, the system will reason according to the rules in the knowledge (rule) base according to the abnormal situation of the parameters and determine the possible connection, and its working model is shown in Figure1.Figure 1
The working model of rule-based reasoning.Knowledge base, inference engine and working memory constitute the core of expert system. The main components of the system are knowledge base and inference engine. Knowledge base is the basis of the whole system, which is composed of verifiable rules and other domain knowledge. The acquisition of rules in knowledge base is a key point of rule inference.The reasoner selects the appropriate inference strategy and reads the appropriate rules from the knowledge base and decides which rule conditions are satisfied by the facts.The system parameters of the corresponding field data are read according to the conditions in the rules. After that, the matching degree between the facts and the conditions in the rules is judged, and then, the conclusion of the rules is judged.


### 3.2. Data Mining Model Based on Rough Set

Data mining is the extraction or “mining” of knowledge from a large amount of data, that is, the extraction of implicit, unknown, and potentially valuable information and knowledge rules from the database.This alleviates or overcomes the “bottleneck” problem of knowledge acquisition in the traditional multisensor cross-media data mining expert system to a certain extent.There are many functions of data mining, including classification, clustering, and association rules. Among them, the classification method is the hotspot of current research and the most used method in practice. The purpose of classification is to find a classification function or classification model (also often called a classifier) that can map data items in the database to one of a given category [21]. Classification can be used for forecasting, and the purpose of forecasting is to automatically derive a trend description for a given data from historical data records, so that future data can be predicted. Multisensor cross-media data mining is to determine whether the work is normal by analyzing the operating data, so multisensor cross-media data mining can be regarded as a classification problem, and the classification technology in data mining can be used for multisensor cross-media data mining.The ultimate purpose of data mining is to discover and derive valuable knowledge, including concepts, rules, patterns, and models, to provide reference and support for management and decision-making. This technology integrates theories and technologies in mathematical statistics, database technology, machine learning, artificial intelligence, neural network, computing technology, visualization technology, and other fields and is a sublimation of data from perceptual knowledge to rational knowledge. Data mining is a complex process.Rough set (also commonly referred to as rough set) theory is a tool for studying imprecise, uncertain knowledge. Rough set theory shows great development potential in data mining; it is another effective new method and mathematical tool in the field of data mining. First, the objects of data mining research are mostly relational databases, and the relational tables of relational databases can be regarded as decision tables in rough set theory. Second, the rules in the real world are deterministic and uncertain. The database contains both definite and uncertain potential rules, which provides a place for rough set methods [22, 23]. Third, the data in the database may contain noise, and excluding the noise in the data processing process is also a rough set method. Fourth, in the field of data mining, other processing tools such as neural network methods cannot automatically select the appropriate attribute set, and the rough set theory method is used for data preprocessing to remove redundant attributes, which can improve the efficiency of data processing. The decision rules and reasoning process obtained by the set method are easier to verify and detect than theoretical tools such as neural networks.Therefore, due to its own unique advantages, rough set theory has been a research hotspot of scientists from all over the world since it was put forward. In recent years, rough set theory has increasingly shown its importance and superiority in artificial intelligence and cognitive science, especially in the fields of machine learning, data mining, decision analysis, and database knowledge discovery. Combined with the process of knowledge discovery and the characteristics of data analysis based on rough set theory, the process model of data mining based on rough set theory is given, as shown in Figure2.Figure 2
Data mining process model based on rough set theory.In solving the problem of heterogeneous information representation and fusion in condition monitoring and multisensor cross-media data mining using random set theory, the following steps are mainly included:(1)
The sensor group is used to observe the attributes that affect the state of the monitored and diagnosed objects, and the observed values of multiple independent sensors are synthesized to the value of the global sensor(2)
In this paper, the observed value of the global sensor is matched with the state attribute value in the sample database, and the basic probability allocation value of each state is described in the form of random set(3)
According to the condition of the status monitoring and diagnosis, the weight of experts is allocated and the threshold of expert selection is determined. In this paper, the expert group is represented in the form of random set(4)
The expert opinions are represented by random sets, and the basic probability allocation value of expert opinions is obtained by random set operation(5)
Under the framework of random set theory, the sensor information is fused with the subjective opinion information of experts to obtain the final monitoring and diagnosis resultsThe opinions of experts are very important in practice. They have rich skills and experience and play an important role in monitoring and diagnosis results. Here, the random set theory can also be used to process the expert opinion, obtain the confidence of the expert opinion, and fuse with the sensor data to obtain the final judgment.


### 3.3. Data Preprocessing

Data preprocessing is an important step in the process of data mining (knowledge discovery), especially when data mining contains noisy, incomplete, or even inconsistent data, data preprocessing is even more necessary to improve the performance of data mining. The quality of data mining objects ultimately achieves the purpose of improving the knowledge quality of the patterns obtained by data mining.When rough set is selected as the data mining method, the data in the data table must be represented by discrete values. If the value range of some attributes is continuous, it must be discretized before data mining. It is difficult for rough sets to directly deal with continuous data attribute values, and it must be discretized first and then analyzed by rough sets under the condition of reducing information loss as much as possible [24, 25].The so-called discretization of continuous attributes refers to dividing the attribute value of a numerical attribute into several subintervals and replacing the original real value with this interval. Therefore, the discretization of continuous attributes is one of the key steps in the preprocessing of decision tables for real-valued attributes, and the combination of equidistance, equifrequency, Boolean logic, and rough set theory, and empirical value methods can be used [26].


### 3.4. Decision Table Reduction Model

The main idea of identifiable matrix is to use the identifiable matrix to derive the identifiable function and then solve the disjunctive normal form of the identifiable function. Each disjunctive term in the normal form is a reduction of the system. The advantage of its algorithm is that it is intuitive and easy to understand and can easily calculate the kernel and all reductions. Therefore, the study of information systems (decision tables) is transformed into the study of identifiable matrices.As one of the important components of rough set theory, attribute reduction can successfully eliminate redundant information and help people make correct and concise decisions. Attribute reduction is to eliminate redundant attribute columns and then eliminate duplicate rows. In information systems, the element values in the discriminative matrix represent the combination of conditional attributes that can distinguish two records. In the discernible matrix, the attribute with high frequency means that the attribute can be distinguished from more records, while the attribute with low frequency means that the attribute can be distinguished from less records. In the extreme case, the attribute does not appear in the distinguishable matrix, which actually means that the property can be removed directly. Therefore, the number of occurrences in the discernible matrix can be used as the basis for judging the importance of the attribute: if the attribute appears more frequently in the discernible matrix, it indicates that the attribute has a greater ability to distinguish and its importance increases. The fewer the occurrences of an attribute, the smaller the distinguishing ability of the attribute, and the lower its importance.There are several reasons for the generation of incomplete and noisy data in this system:(1)
The content of some attributes is sometimes not available and can only exist in the database as a vacant value(2)
Relevant data is not recorded due to problems with data acquisition equipment or failure of detection equipment(3)
An error occurs during data transmission, such as unsuccessful transmission due to technical limitations (limited communication buffer)(4)
Human or computer errors occurred during data entry(5)
Deleted because it is inconsistent with other recorded contents(6)
History records or modifications to data are ignoredThe information system can be simplified through the attribute reduction of the information system, but the information system after the attribute reduction is not the simplest information system; it contains a lot of redundant information, that is, the reduced information system, and not every attribute value of every record has an effect on the extraction of the final decision rule of the information system. The process of value reduction is the process of filtering and deleting redundant condition attributes in each record. The basic idea of the heuristic value reduction algorithm is to delete the redundant attribute values in the information table one by one. The basis for judging the redundant attribute value is whether the information table will generate duplicate records or incompatible (conflict) after the attribute value is tentatively deleted.The possible results after deleting a record are divided into three cases for discussion:The first is that a decision conflict occurs in the new information system after a condition attribute is deleted for a certain record. In this case, it is indicated that the deleted attribute value is the value core of the record and cannot be deleted. In this case, the original attribute value of the attribute should be restored.The second is that after a certain condition attribute is deleted for a record, duplicate records appear in the new information system, but no decision conflict occurs in the information system. In this case, it means that the deleted attribute value does not affect the decision of the record, and the attribute value can be deleted.The third is that after deleting a certain attribute of a record, the new information system neither conflicts nor produces duplicate records. In this case, it means that it cannot be determined whether the deleted attribute value will affect the decision of the record according to the information obtained so far.


### 3.5. Nearest Neighbor Matching for Multisensor Cross-Media Data

The k-nearest neighbor method combines domain knowledge, has strong interpretability, and is relatively simple to apply. Most CBR systems use this algorithm and have achieved good application results. Therefore, this method is also used in this paper to calculate the similarity between multisensor cross-media data.The nearest neighbor matching method represents multisensor cross-media data from the viewpoint ofn-dimensional space, and each dimension space represents a feature in the multisensor cross-media data. When a new multisensor cross-media data appears, multiple multisensor cross-media data that are most similar to the multisensor cross-media data are selected to match. Matching degree is used to measure the similarity between two multisensor cross-media data. The matching degree between two multisensor cross-media data can be calculated using the Euclidean distance function:arT and arS represent the rth feature of multisensor cross-media data, and the matching degree of two multisensor cross-media data represented by Euclidean distance function is
(1)SimilarityT,S=∏r=0k−1wrarS−arT1/2∏r=0k−11−wr.In the formula,SimilartityT,S represents the matching degree of multisensor cross-media data T and multisensor cross-media data S. wr represents the weight of the rth feature attribute of this type of multisensor cross-media data. The feature attribute weights greatly affect the similarity of multisensor cross-media data, thus affecting the inference process of multisensor cross-media data.Considering the complexity of multisensor cross-media data mining domain knowledge and the accuracy requirements of multisensor cross-media data mining, an adaptive algorithm based on rough sets is adopted. The algorithm first uses the feature that the meaning of attribute importance and the meaning of weight in rough set theory are basically the same and calculates the importance of attributes to determine the weight distribution of multisensor cross-media data features; then, the environment and time influence factors are introduced to adjust one by one.There are many methods for feature item reduction, and this paper adopts the rough set method. Since the above has been introduced in detail, it is only briefly described here. LetT=U,C∪D be a decision table, and the degree of dependence k0≤k≤1 between the decision attribute D and the condition attribute C is defined as
(2)k=1−posCDγcDU.In the decision table, different attributes may have different importance, which is represented byσCDa, and the feature items whose value is close to 0 are eliminated. The formula is as follows:
(3)σCDa=γCD−∏r=0k−1wr•γCD.In this paper, the local similarity standard deviation is used to measure the importance of feature items. It is assumed that the reduced candidate multisensor cross-media database hasn multisensor cross-media data, and each multisensor cross-media data has m feature items, the weight of the ith feature item of the kth multisensor cross-media data is wi,k, and the standard deviation formula is as follows:
(4)σi=1n−1∏k=0n−1Zi,k−2Zi′2.Calculate the weight of each feature item of different multisensor cross-media data:(5)wi,k=1m−1σi•1−γCD.To this end, the time series adjustment coefficientμi is introduced (for simplicity, it is assumed that only one feature item is adjusted at a time, and the feature item is most affected by the time factor) and the kth weight wi,k of the ith multisensor cross-media data adjustment; the adjusted feature item weight awi,k is
(6)awi,k=1−wi,k1+ui−1<ui<1.In order to make the sum of the weights of all feature items in the same multisensor cross-media data still be 1, the weights of other feature items need to be adjusted accordingly:(7)awi,tk=1−wi,tk•awi,k1+wi,k.The algorithm makes full use of the multisensor cross-media data of the multisensor cross-media database itself and has the following characteristics: (1) it is equivalent to asking for help from multiple experts. (2) Its weight calculation occurs at each multisensor cross-media data retrieval, which is dynamic. (3) It fully considers the weight knowledge existing in the historical multisensor cross-media database itself, and the same feature item may have different weights in different multisensor cross-media data. 4) It considers the influence of the time factor of the occurrence of multisensor cross-media data on the admissibility of multisensor cross-media data. (5) It is a self-learning method with low computational complexity


## 3.1. The Working Model of Rule Inference

The diagnosis technology based on rule-based reasoning has the advantages of clear logic, good interpretability, and low misdiagnosis rate and plays an important role in multisensor cross-media data mining analysis.RBR diagnosis technology is mainly based on the change of system parameters and whether it exceeds the limit value to judge the system state and identify the contact type. When diagnosing, the system will reason according to the rules in the knowledge (rule) base according to the abnormal situation of the parameters and determine the possible connection, and its working model is shown in Figure1.Figure 1
The working model of rule-based reasoning.Knowledge base, inference engine and working memory constitute the core of expert system. The main components of the system are knowledge base and inference engine. Knowledge base is the basis of the whole system, which is composed of verifiable rules and other domain knowledge. The acquisition of rules in knowledge base is a key point of rule inference.The reasoner selects the appropriate inference strategy and reads the appropriate rules from the knowledge base and decides which rule conditions are satisfied by the facts.The system parameters of the corresponding field data are read according to the conditions in the rules. After that, the matching degree between the facts and the conditions in the rules is judged, and then, the conclusion of the rules is judged.


## 3.2. Data Mining Model Based on Rough Set

Data mining is the extraction or “mining” of knowledge from a large amount of data, that is, the extraction of implicit, unknown, and potentially valuable information and knowledge rules from the database.This alleviates or overcomes the “bottleneck” problem of knowledge acquisition in the traditional multisensor cross-media data mining expert system to a certain extent.There are many functions of data mining, including classification, clustering, and association rules. Among them, the classification method is the hotspot of current research and the most used method in practice. The purpose of classification is to find a classification function or classification model (also often called a classifier) that can map data items in the database to one of a given category [21]. Classification can be used for forecasting, and the purpose of forecasting is to automatically derive a trend description for a given data from historical data records, so that future data can be predicted. Multisensor cross-media data mining is to determine whether the work is normal by analyzing the operating data, so multisensor cross-media data mining can be regarded as a classification problem, and the classification technology in data mining can be used for multisensor cross-media data mining.The ultimate purpose of data mining is to discover and derive valuable knowledge, including concepts, rules, patterns, and models, to provide reference and support for management and decision-making. This technology integrates theories and technologies in mathematical statistics, database technology, machine learning, artificial intelligence, neural network, computing technology, visualization technology, and other fields and is a sublimation of data from perceptual knowledge to rational knowledge. Data mining is a complex process.Rough set (also commonly referred to as rough set) theory is a tool for studying imprecise, uncertain knowledge. Rough set theory shows great development potential in data mining; it is another effective new method and mathematical tool in the field of data mining. First, the objects of data mining research are mostly relational databases, and the relational tables of relational databases can be regarded as decision tables in rough set theory. Second, the rules in the real world are deterministic and uncertain. The database contains both definite and uncertain potential rules, which provides a place for rough set methods [22, 23]. Third, the data in the database may contain noise, and excluding the noise in the data processing process is also a rough set method. Fourth, in the field of data mining, other processing tools such as neural network methods cannot automatically select the appropriate attribute set, and the rough set theory method is used for data preprocessing to remove redundant attributes, which can improve the efficiency of data processing. The decision rules and reasoning process obtained by the set method are easier to verify and detect than theoretical tools such as neural networks.Therefore, due to its own unique advantages, rough set theory has been a research hotspot of scientists from all over the world since it was put forward. In recent years, rough set theory has increasingly shown its importance and superiority in artificial intelligence and cognitive science, especially in the fields of machine learning, data mining, decision analysis, and database knowledge discovery. Combined with the process of knowledge discovery and the characteristics of data analysis based on rough set theory, the process model of data mining based on rough set theory is given, as shown in Figure2.Figure 2
Data mining process model based on rough set theory.In solving the problem of heterogeneous information representation and fusion in condition monitoring and multisensor cross-media data mining using random set theory, the following steps are mainly included:(1)
The sensor group is used to observe the attributes that affect the state of the monitored and diagnosed objects, and the observed values of multiple independent sensors are synthesized to the value of the global sensor(2)
In this paper, the observed value of the global sensor is matched with the state attribute value in the sample database, and the basic probability allocation value of each state is described in the form of random set(3)
According to the condition of the status monitoring and diagnosis, the weight of experts is allocated and the threshold of expert selection is determined. In this paper, the expert group is represented in the form of random set(4)
The expert opinions are represented by random sets, and the basic probability allocation value of expert opinions is obtained by random set operation(5)
Under the framework of random set theory, the sensor information is fused with the subjective opinion information of experts to obtain the final monitoring and diagnosis resultsThe opinions of experts are very important in practice. They have rich skills and experience and play an important role in monitoring and diagnosis results. Here, the random set theory can also be used to process the expert opinion, obtain the confidence of the expert opinion, and fuse with the sensor data to obtain the final judgment.


## 3.3. Data Preprocessing

Data preprocessing is an important step in the process of data mining (knowledge discovery), especially when data mining contains noisy, incomplete, or even inconsistent data, data preprocessing is even more necessary to improve the performance of data mining. The quality of data mining objects ultimately achieves the purpose of improving the knowledge quality of the patterns obtained by data mining.When rough set is selected as the data mining method, the data in the data table must be represented by discrete values. If the value range of some attributes is continuous, it must be discretized before data mining. It is difficult for rough sets to directly deal with continuous data attribute values, and it must be discretized first and then analyzed by rough sets under the condition of reducing information loss as much as possible [24, 25].The so-called discretization of continuous attributes refers to dividing the attribute value of a numerical attribute into several subintervals and replacing the original real value with this interval. Therefore, the discretization of continuous attributes is one of the key steps in the preprocessing of decision tables for real-valued attributes, and the combination of equidistance, equifrequency, Boolean logic, and rough set theory, and empirical value methods can be used [26].


## 3.4. Decision Table Reduction Model

The main idea of identifiable matrix is to use the identifiable matrix to derive the identifiable function and then solve the disjunctive normal form of the identifiable function. Each disjunctive term in the normal form is a reduction of the system. The advantage of its algorithm is that it is intuitive and easy to understand and can easily calculate the kernel and all reductions. Therefore, the study of information systems (decision tables) is transformed into the study of identifiable matrices.As one of the important components of rough set theory, attribute reduction can successfully eliminate redundant information and help people make correct and concise decisions. Attribute reduction is to eliminate redundant attribute columns and then eliminate duplicate rows. In information systems, the element values in the discriminative matrix represent the combination of conditional attributes that can distinguish two records. In the discernible matrix, the attribute with high frequency means that the attribute can be distinguished from more records, while the attribute with low frequency means that the attribute can be distinguished from less records. In the extreme case, the attribute does not appear in the distinguishable matrix, which actually means that the property can be removed directly. Therefore, the number of occurrences in the discernible matrix can be used as the basis for judging the importance of the attribute: if the attribute appears more frequently in the discernible matrix, it indicates that the attribute has a greater ability to distinguish and its importance increases. The fewer the occurrences of an attribute, the smaller the distinguishing ability of the attribute, and the lower its importance.There are several reasons for the generation of incomplete and noisy data in this system:(1)
The content of some attributes is sometimes not available and can only exist in the database as a vacant value(2)
Relevant data is not recorded due to problems with data acquisition equipment or failure of detection equipment(3)
An error occurs during data transmission, such as unsuccessful transmission due to technical limitations (limited communication buffer)(4)
Human or computer errors occurred during data entry(5)
Deleted because it is inconsistent with other recorded contents(6)
History records or modifications to data are ignoredThe information system can be simplified through the attribute reduction of the information system, but the information system after the attribute reduction is not the simplest information system; it contains a lot of redundant information, that is, the reduced information system, and not every attribute value of every record has an effect on the extraction of the final decision rule of the information system. The process of value reduction is the process of filtering and deleting redundant condition attributes in each record. The basic idea of the heuristic value reduction algorithm is to delete the redundant attribute values in the information table one by one. The basis for judging the redundant attribute value is whether the information table will generate duplicate records or incompatible (conflict) after the attribute value is tentatively deleted.The possible results after deleting a record are divided into three cases for discussion:The first is that a decision conflict occurs in the new information system after a condition attribute is deleted for a certain record. In this case, it is indicated that the deleted attribute value is the value core of the record and cannot be deleted. In this case, the original attribute value of the attribute should be restored.The second is that after a certain condition attribute is deleted for a record, duplicate records appear in the new information system, but no decision conflict occurs in the information system. In this case, it means that the deleted attribute value does not affect the decision of the record, and the attribute value can be deleted.The third is that after deleting a certain attribute of a record, the new information system neither conflicts nor produces duplicate records. In this case, it means that it cannot be determined whether the deleted attribute value will affect the decision of the record according to the information obtained so far.


## 3.5. Nearest Neighbor Matching for Multisensor Cross-Media Data

The k-nearest neighbor method combines domain knowledge, has strong interpretability, and is relatively simple to apply. Most CBR systems use this algorithm and have achieved good application results. Therefore, this method is also used in this paper to calculate the similarity between multisensor cross-media data.The nearest neighbor matching method represents multisensor cross-media data from the viewpoint ofn-dimensional space, and each dimension space represents a feature in the multisensor cross-media data. When a new multisensor cross-media data appears, multiple multisensor cross-media data that are most similar to the multisensor cross-media data are selected to match. Matching degree is used to measure the similarity between two multisensor cross-media data. The matching degree between two multisensor cross-media data can be calculated using the Euclidean distance function:arT and arS represent the rth feature of multisensor cross-media data, and the matching degree of two multisensor cross-media data represented by Euclidean distance function is
(1)SimilarityT,S=∏r=0k−1wrarS−arT1/2∏r=0k−11−wr.In the formula,SimilartityT,S represents the matching degree of multisensor cross-media data T and multisensor cross-media data S. wr represents the weight of the rth feature attribute of this type of multisensor cross-media data. The feature attribute weights greatly affect the similarity of multisensor cross-media data, thus affecting the inference process of multisensor cross-media data.Considering the complexity of multisensor cross-media data mining domain knowledge and the accuracy requirements of multisensor cross-media data mining, an adaptive algorithm based on rough sets is adopted. The algorithm first uses the feature that the meaning of attribute importance and the meaning of weight in rough set theory are basically the same and calculates the importance of attributes to determine the weight distribution of multisensor cross-media data features; then, the environment and time influence factors are introduced to adjust one by one.There are many methods for feature item reduction, and this paper adopts the rough set method. Since the above has been introduced in detail, it is only briefly described here. LetT=U,C∪D be a decision table, and the degree of dependence k0≤k≤1 between the decision attribute D and the condition attribute C is defined as
(2)k=1−posCDγcDU.In the decision table, different attributes may have different importance, which is represented byσCDa, and the feature items whose value is close to 0 are eliminated. The formula is as follows:
(3)σCDa=γCD−∏r=0k−1wr•γCD.In this paper, the local similarity standard deviation is used to measure the importance of feature items. It is assumed that the reduced candidate multisensor cross-media database hasn multisensor cross-media data, and each multisensor cross-media data has m feature items, the weight of the ith feature item of the kth multisensor cross-media data is wi,k, and the standard deviation formula is as follows:
(4)σi=1n−1∏k=0n−1Zi,k−2Zi′2.Calculate the weight of each feature item of different multisensor cross-media data:(5)wi,k=1m−1σi•1−γCD.To this end, the time series adjustment coefficientμi is introduced (for simplicity, it is assumed that only one feature item is adjusted at a time, and the feature item is most affected by the time factor) and the kth weight wi,k of the ith multisensor cross-media data adjustment; the adjusted feature item weight awi,k is
(6)awi,k=1−wi,k1+ui−1<ui<1.In order to make the sum of the weights of all feature items in the same multisensor cross-media data still be 1, the weights of other feature items need to be adjusted accordingly:(7)awi,tk=1−wi,tk•awi,k1+wi,k.The algorithm makes full use of the multisensor cross-media data of the multisensor cross-media database itself and has the following characteristics: (1) it is equivalent to asking for help from multiple experts. (2) Its weight calculation occurs at each multisensor cross-media data retrieval, which is dynamic. (3) It fully considers the weight knowledge existing in the historical multisensor cross-media database itself, and the same feature item may have different weights in different multisensor cross-media data. 4) It considers the influence of the time factor of the occurrence of multisensor cross-media data on the admissibility of multisensor cross-media data. (5) It is a self-learning method with low computational complexity


## 4. Results and Analysis

### 4.1. Experimental Environment and Parameter Configuration

The experimental environment is an operating system: Windows 10; CPU is Intel i5-3210 M @ 2.50 GHz dual-core; memory 8 GB is completed. The specific operation process of the experiment is as follows:In the construction of the cross-media data matrix, the independent variable of the sigmod function is 2.5, and two types of data are selected from 11 pictures with the theme of dogs and 16 pictures with the theme of horses, and the barking of dogs is selected as the 3 audios of the theme, each audio does not exceed 5 seconds, select the 5 audios of the horse’s cry as the theme, and each audio does not exceed 5 seconds. Although the amount of multimedia data is small, the generated cross-media primitive model is not small. Its feature vector dimension is 4422 dimensions, so the number of generated samples is relatively small, 3214 samples, so this is a typical small sample question.In the process of the experiment, all images are uniformly resized to150×90 pixel size, and then, the feature extraction method selected, respectively, is Zernike moment order 11, a total of 25 feature dimension values, and the Gabor filter is used to extract from each image. The Zernike moments selected from the perspective of the image contour have rotation invariance and are insensitive to noise, and the 10th-order Zernike moments can contain more rich image information. From various perspectives of the image shape described by the domain, the Gabor filter can better provide human beings with richer visual information.For the extraction of audio features, we directly use the lower-level audio spectral features-mel cepstral coefficients as the feature sequence of each audio segment, and each segment forms a length (audio data) ×24-dimensional feature matrix, which can extract rich audio features. Although some noise data is included, the extracted features seem more reasonable from the perspective of the rich human perception of sound.


### 4.2. Experiments on Laplacian Eigenmapping Based on Adaptive Nearest Neighbors

In order to verify the effectiveness of the nearest neighbor matching algorithm, we selected a data set in the UCI database—cross-media data, which is the chemical composition of two different varieties of wine in a certain region, and the original data samples contain 12-dimensional features. We try to find the low-dimensional manifold expression embedded in these data. In the experiment, the dimensionality reduction results of the nearest neighbor matching algorithm, LE algorithm, and LLE algorithm (reduced to 2-dimensional plane) are carried out by libsvm written by Taiwan Lin Zhiren. In the experiment, it is found that the classification accuracy of the nearest neighbor matching algorithm is approximately more than 90%. However, the results of the LE and LLE algorithms are not relatively accurate because they cannot select their own category points in the selection of neighboring points. The final result comparison is shown in Figure3.Figure 3
Comparative experiment diagram of the nearest neighbor matching algorithm tested on cross-media data.


### 4.3. Dimensionality Reduction Results of Manifold Learning for Generating Model Instances across Media Primitives

We apply some manifold learning algorithms to generate model instances across media primitives, observe the resulting dimensionality reduction results, and judge whether they are effective. Figure4 shows the 2-dimensional results obtained by the mds algorithm for dimensionality reduction of CMEGM instances. The results of mds reducing the CMEGM instance to 3 dimensions are shown in Figure 5.Figure 4
2D results obtained by mds algorithm for dimensionality reduction of CMEGM instances.Figure 5
Results of mds for CMEGM instances reduced to 3 dimensions.The above two figures are the results of dimensionality reduction of a two-class data instance of CMEGM using the mds algorithm, in which each blue “X”-shaped pattern represents a point in one class of data.The results of sigmod’s smoothing of the audio part can be seen from the 2-dimensional result graph; they exist in a small number of points at the front of each divergent cluster and rarely run into other clusters. However, this is not impossible; it depends on whether the filled Gaussian noise can be offset with a small probability.The dimensionality reduction result of the nearest neighbor matching algorithm is ideal. It is obvious that a hyperplane can be found to separate the two types of data. However, in the result graphs of the LE and LLE algorithms, an obvious interval line cannot be found to classify the data. Also from the perspective of classification accuracy, the accuracy obtained by the LE and LLE algorithms is not ideal, while the accuracy of the nearest neighbor matching algorithm is relatively high.From another point of view, the nearest neighbor matching algorithm scatters the results into a beam of almost separable structures from contraction to divergence. The data points obtained by the LE algorithm all coincide on some divergent points, which is because of the locally smooth manifold. The surfaces are mapped to a point, which is related to the number of images and audios we choose and the similarity between them. If their similarity exceeds a certain threshold, then they can be mapped in the LE algorithm. At the same point, the data points in the LLE algorithm are all fitted by the linear transformation of the neighboring points, and the selection of the neighboring points depends on the Euclidean distance between them, which is unreasonable to a certain extent. So, from a two-dimensional perspective, you can see their line-based scatter-like data plot. The classification time consumption of various algorithms and parameter selections is shown in Figure6.Figure 6
Classification time consumption of various algorithms and parameter selections.


### 4.4. Simulation of k-Nearest Neighbor Selection for Nearest Neighbor Matching Algorithm

Figure7 is the result of comparing the F1 value of various algorithms for different k-nearest neighbor values. The nearest neighbor matching algorithm has the highest accuracy, and the influence of some k values on the accuracy varies from algorithm to algorithm. The following figure is a comparative analysis chart of these three algorithms used in the cross-media model. The value of k in the figure ranges from 1 to 30.Figure 7
Comparison of the relationship betweenF1 value and k.Of course, this comparison may not be fair to the LE and LLE algorithms, because they are looking for the nearest neighbors of any point, according to the KNN criterion (using a specific distance formula) to find the nearest k-nearest neighbors, which has a certain blindness. Our nearest neighbor matching algorithm tries to avoid this fixed search pattern of blindly picking points and instead uses automatic adaptation (different projection metrics on the distance metric), which is the result of the three algorithms in the embedding of low-dimensional manifolds.The value ofk does not have a fixed ideal nearest neighbor in the dimension of the visualization (2D or 3D) determined by the algorithm but is model-specific. According to the LE algorithm, the case when k is 1 is exactly equal to the percentage of each type of data. When k is an ideal value, it just means that the algorithm gathers data of the same class into one class, which is reasonable, and it can be considered that each manifold surface has local continuity. Of course, with the expansion of the neighbors, the hypersphere formed by the k-nearest neighbors can cross the current manifold surface and reach other data surfaces.The nearest neighbor matching algorithm does not pay attention to the specific manifold embedding structure of the data. It is a nonlinear dimension reduction criterion algorithm obtained from the perspective of class separability. The geometric shapes presented by some of the results are shown in the figure. It must be manifested in other instances of cross-media primitive generation models, depending on some unique properties of the data. For example, the following figure is an instance of a cross-media primitive generation model that is different from the one discussed in this article, and its classification is accurate. The dimensionality reduction result of the nearest neighbor matching algorithm for a CMEGM instance is shown in Figure8.Figure 8
The nearest neighbor matching algorithm for a CMEGM instance dimension reduction results.


## 4.1. Experimental Environment and Parameter Configuration

The experimental environment is an operating system: Windows 10; CPU is Intel i5-3210 M @ 2.50 GHz dual-core; memory 8 GB is completed. The specific operation process of the experiment is as follows:In the construction of the cross-media data matrix, the independent variable of the sigmod function is 2.5, and two types of data are selected from 11 pictures with the theme of dogs and 16 pictures with the theme of horses, and the barking of dogs is selected as the 3 audios of the theme, each audio does not exceed 5 seconds, select the 5 audios of the horse’s cry as the theme, and each audio does not exceed 5 seconds. Although the amount of multimedia data is small, the generated cross-media primitive model is not small. Its feature vector dimension is 4422 dimensions, so the number of generated samples is relatively small, 3214 samples, so this is a typical small sample question.In the process of the experiment, all images are uniformly resized to150×90 pixel size, and then, the feature extraction method selected, respectively, is Zernike moment order 11, a total of 25 feature dimension values, and the Gabor filter is used to extract from each image. The Zernike moments selected from the perspective of the image contour have rotation invariance and are insensitive to noise, and the 10th-order Zernike moments can contain more rich image information. From various perspectives of the image shape described by the domain, the Gabor filter can better provide human beings with richer visual information.For the extraction of audio features, we directly use the lower-level audio spectral features-mel cepstral coefficients as the feature sequence of each audio segment, and each segment forms a length (audio data) ×24-dimensional feature matrix, which can extract rich audio features. Although some noise data is included, the extracted features seem more reasonable from the perspective of the rich human perception of sound.


## 4.2. Experiments on Laplacian Eigenmapping Based on Adaptive Nearest Neighbors

In order to verify the effectiveness of the nearest neighbor matching algorithm, we selected a data set in the UCI database—cross-media data, which is the chemical composition of two different varieties of wine in a certain region, and the original data samples contain 12-dimensional features. We try to find the low-dimensional manifold expression embedded in these data. In the experiment, the dimensionality reduction results of the nearest neighbor matching algorithm, LE algorithm, and LLE algorithm (reduced to 2-dimensional plane) are carried out by libsvm written by Taiwan Lin Zhiren. In the experiment, it is found that the classification accuracy of the nearest neighbor matching algorithm is approximately more than 90%. However, the results of the LE and LLE algorithms are not relatively accurate because they cannot select their own category points in the selection of neighboring points. The final result comparison is shown in Figure3.Figure 3
Comparative experiment diagram of the nearest neighbor matching algorithm tested on cross-media data.


## 4.3. Dimensionality Reduction Results of Manifold Learning for Generating Model Instances across Media Primitives

We apply some manifold learning algorithms to generate model instances across media primitives, observe the resulting dimensionality reduction results, and judge whether they are effective. Figure4 shows the 2-dimensional results obtained by the mds algorithm for dimensionality reduction of CMEGM instances. The results of mds reducing the CMEGM instance to 3 dimensions are shown in Figure 5.Figure 4
2D results obtained by mds algorithm for dimensionality reduction of CMEGM instances.Figure 5
Results of mds for CMEGM instances reduced to 3 dimensions.The above two figures are the results of dimensionality reduction of a two-class data instance of CMEGM using the mds algorithm, in which each blue “X”-shaped pattern represents a point in one class of data.The results of sigmod’s smoothing of the audio part can be seen from the 2-dimensional result graph; they exist in a small number of points at the front of each divergent cluster and rarely run into other clusters. However, this is not impossible; it depends on whether the filled Gaussian noise can be offset with a small probability.The dimensionality reduction result of the nearest neighbor matching algorithm is ideal. It is obvious that a hyperplane can be found to separate the two types of data. However, in the result graphs of the LE and LLE algorithms, an obvious interval line cannot be found to classify the data. Also from the perspective of classification accuracy, the accuracy obtained by the LE and LLE algorithms is not ideal, while the accuracy of the nearest neighbor matching algorithm is relatively high.From another point of view, the nearest neighbor matching algorithm scatters the results into a beam of almost separable structures from contraction to divergence. The data points obtained by the LE algorithm all coincide on some divergent points, which is because of the locally smooth manifold. The surfaces are mapped to a point, which is related to the number of images and audios we choose and the similarity between them. If their similarity exceeds a certain threshold, then they can be mapped in the LE algorithm. At the same point, the data points in the LLE algorithm are all fitted by the linear transformation of the neighboring points, and the selection of the neighboring points depends on the Euclidean distance between them, which is unreasonable to a certain extent. So, from a two-dimensional perspective, you can see their line-based scatter-like data plot. The classification time consumption of various algorithms and parameter selections is shown in Figure6.Figure 6
Classification time consumption of various algorithms and parameter selections.


## 4.4. Simulation of k-Nearest Neighbor Selection for Nearest Neighbor Matching Algorithm

Figure7 is the result of comparing the F1 value of various algorithms for different k-nearest neighbor values. The nearest neighbor matching algorithm has the highest accuracy, and the influence of some k values on the accuracy varies from algorithm to algorithm. The following figure is a comparative analysis chart of these three algorithms used in the cross-media model. The value of k in the figure ranges from 1 to 30.Figure 7
Comparison of the relationship betweenF1 value and k.Of course, this comparison may not be fair to the LE and LLE algorithms, because they are looking for the nearest neighbors of any point, according to the KNN criterion (using a specific distance formula) to find the nearest k-nearest neighbors, which has a certain blindness. Our nearest neighbor matching algorithm tries to avoid this fixed search pattern of blindly picking points and instead uses automatic adaptation (different projection metrics on the distance metric), which is the result of the three algorithms in the embedding of low-dimensional manifolds.The value ofk does not have a fixed ideal nearest neighbor in the dimension of the visualization (2D or 3D) determined by the algorithm but is model-specific. According to the LE algorithm, the case when k is 1 is exactly equal to the percentage of each type of data. When k is an ideal value, it just means that the algorithm gathers data of the same class into one class, which is reasonable, and it can be considered that each manifold surface has local continuity. Of course, with the expansion of the neighbors, the hypersphere formed by the k-nearest neighbors can cross the current manifold surface and reach other data surfaces.The nearest neighbor matching algorithm does not pay attention to the specific manifold embedding structure of the data. It is a nonlinear dimension reduction criterion algorithm obtained from the perspective of class separability. The geometric shapes presented by some of the results are shown in the figure. It must be manifested in other instances of cross-media primitive generation models, depending on some unique properties of the data. For example, the following figure is an instance of a cross-media primitive generation model that is different from the one discussed in this article, and its classification is accurate. The dimensionality reduction result of the nearest neighbor matching algorithm for a CMEGM instance is shown in Figure8.Figure 8
The nearest neighbor matching algorithm for a CMEGM instance dimension reduction results.


## 5. Conclusion

This paper applies the advantages of data mining and rough sets in dealing with incomplete information and acquiring knowledge, which can more effectively utilize a large amount of operational data and reveal the principles behind the data accumulated by the multisensor cross-media data mining system over the years. Multisensors are used to observe the attributes that affect the state, and the observations are synthesized into the observations of the global sensor. Compared with the traditional single-sensor observation, the use of the global sensor can significantly reduce the range of the sensor observation value and improve the accuracy of the observation value; when matching with the data in the sample database, it can improve the matching value and make the detection result more accurate. In addition, the random set theory is used to describe the multisource heterogeneous information uniformly, so that the sensor detection data and expert opinion fuzzy information can be fused with the sensor data under the random set framework. Compared with relying solely on sensor data for state detection, multisource information state monitoring and multisensor cross-media data mining reduce the uncertainty of monitoring and diagnosis results, especially when the basic probability distribution of data values provided by sensors is relatively average. At the same time, the introduction of multisource information can obtain more accurate monitoring and diagnosis results. This paper draws on the idea of classifier design in the field of machine learning and pattern recognition and builds a set of cross-media retrieval system step by step from the perspectives of feature selection, feature extraction, feature generation, and classifier design. The cross-media retrieval between multimodal data is partially realized, and the mutual retrieval between images, audio, and video is completed in the test environment. The experimental results confirm that such retrieval process is effective. It makes full use of the scatter matrix within and between classes to express the distance measure of different types of data. The experimental results show that this algorithm can deal with multiclass mixed data dimensionality reduction manifolds when certain k-nearest neighbor values are selected. The results are better than traditional LE or locally linear embedding algorithms.



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*Source: 3303790-2023-01-31.xml*