Welfare with IoT Technology Using Fuzzy Logic

We present some model theoretic results for Lukasiewicz predicate logic by using the methods of continuous model theory developed by Chang and Keisler. We prove compactness theorem with respect to the class of all structures taking values in the Lukasiewicz BL-algebra. We also prove some appropriate preservation theorems concerning universal and inductive theories. Finally, Skolemization and Morleyization in this framework are discussed and some natural examples of fuzzy theories are presented. ajek and Cintula wrote: \In the last few decades many formal systems of fuzzy logics have been developed. Since the main dierences between fuzzy and classical logics lie at propositional level, the fuzzy predicate logics have developed more slowly (compared to the propositional ones). In this paper we aim to promote interest in fuzzy predicate logics by contributing to the model theory of fuzzy predicate In that paper the authors proved a generalized completeness result for a wide class of fuzzy predicate logics, namely core fuzzy logics. To do this they used a generalized Henkin method for a through survey of the present status of fuzzy predicate logic, see (3). In the present paper we continue the work on fuzzy predicate logics by developing further model theory of these logics. We choose Lukasiewicz predicate logic. The reason is that the Lukasiewicz BL-algebra is equipped with continuous operations and this is essential in proving the compactness theorem which is a fundamental result in model theory. Here, the compactness theorem says that if each nite subset of a set of sentences has a Lukasiewicz model, then the set itself has a Lukasiewicz model. As consequences of compactness, we prove the most basic model theoretic results including the upward and downward Lowenheim-Skolem theorems. We also prove suitable versions of the most well known preservation theorems, including preservation theorems for universal and inductive theories. At the end, we introduce some fuzzy theories and study their model theoretic properties. To prove the compactness theorem, we use ultraproduct and its basic properties which we rst investigate. We follow the old methods developed by Chang and

Fuzzy Logic (Fuzzy Logic) is a branch of science based on thinking like human beings and solving them with mathematical functions. Fuzzy logic theory is a mathematical theory. Based on fuzzy set theory, it also uses intermediate values. The fuzzy logic that emerged in 1965 is used in many fields. In the production of pacemakers, in the production of artificial organs, in many electronic devices, company efficiency estimation, etc. situations are used. Fuzzy logic, which is frequently used in the solution of problems that occur in uncertain situations such as quality assessment in recent years, is one of the artificial intelligence methods. With the help of machines, people-specific data and experiences are studied using the fuzzy logic approach. In this study, by using Matlab Fuzzy Toolbox, it was aimed to design a system that gives information about the breeding performances of cows. The expert system was designed based on the optimal values under the ideal conditions specified in the literature. The architecture of the system presented in this paper is designed as three input parameters and one output. The designed system was tested with 100 sample values. Afterwards, expert results were evaluated and system decisions were compared. The success of the decision support system was 94%. As a result, the reproductive efficiency of cows can be determined with this designed system. With this determination, the handling or disposal of cows can be determined.

The major disease caused by human death nowadays is heart disease, due it happens suddenly and without significant symptoms, leads patient to miss the best time for first aid. With the development of IoT technology combined with the healthcare industry. It is providing technical support for clinic staff to predict and monitor heart disease patients remotely. In this paper, the main goal is to review the most relevant and latest papers to find the advantages and disadvantages and gaps in this area. Furthermore, compare the different proposed method's performance and present the best framework for heart disease continuous prediction and monitoring. Many researchers have been already providing the use of different types of machine learning algorithms to predict and diagnose heart disease. However, most of the previous researchers use the data collected from the dataset. As well know, to process the data collected from IoT sensors is harder than data collected from the dataset, because it may contain more noise and missing values in IoT sensor collected data. Dealing with those issues is the main challenge in the whole prediction system. Therefore, in this paper, we expect to reduce the research gap to find the best way to continuously monitoring and predicting patient ECG signals collected from IoT sensor devices in the meantime achieved acceptable prediction accuracy.

In this article it has been tried to show that fuzzy theory performs better than probability theory in monitoring the product quality.A method that uses statistical techniques to monitor and control product quality is called statistical process control (SPC), where control charts are test tools frequently used for monitoring the manufacturing process.In this study, statistical quality control and the fuzzy set theory are aimed to combine.As known, fuzzy sets and fuzzy logic are powerful mathematical tools for modeling uncertain systems in industry, nature and humanity; and facilitators for common-sense reasoning in decision making in the absence of complete and precise information.In this basis for a textile firm for monitoring the yarn quality, control charts according to fuzzy theory by considering the quality in terms of grades of conformance as opposed to absolute conformance and nonconformance.And then with the same data for a textile factory, the control chart based on probability theory is constructed.The results of control charts based on two different approaches are compared.It's seen that fuzzy theory performs better than probability theory in monitoring the product quality.

Several applications need to stock and interrogate fuzzy data in a flexible manner. Unfortunately, these two requirements are not supported by the database management systems which currently dominate the databases servers market. Several works have tried to cure these insufficiencies; we speak henceforth of fuzzy databases and flexible querying of these databases. The design problem of such databases did not receive the interest that it deserves. The fuzzy data types are numerous and varied what puts the designer of a fuzzy database in front of a complex problem of choice. In this paper, we propose an approach based on an expert system which helps the designer to choose the appropriate data types for the columns susceptible to stock fuzzy data or interrogated by flexible queries. This solution suits the migration toward a fuzzy database as well as a fuzzy database under design. Although this solution is based on the fuzzy model GEFRED (GEneralised model for Fuzzy RElational Database), it can be applied to other fuzzy models. This expert system can be integrated into the classic design environments like Rational Rose, Power AMC, etc.

Shallots are a high economic commodity that needs special attention because this plant is a high-risk horticultural commodity and is in the short-lived plant category. Moreover, shallot growth relies on environmental variables, such as temperature, soil moisture, soil pH, and humidity. However, Indonesian farmers have difficulties maintaining shallot growth since they only rely on the weather and do not use special equipment to measure the soil condition. Therefore, the monitoring and controlling system is required to be a solution for maintaining the growth parameters of shallot. This study proposed a system that could control and monitor the soil conditions of the shallot based on IoT technology and applied context-aware and fuzzy logic algorithms to control the actuator. The proposed system was developed to control the soil conditions using three specific liquids: neutral water, pH-lowering liquid, and pH-increasing liquid. These liquids were utilized to control the soil pH, soil temperature, and soil moisture. A microcontroller controlled them according to the context-aware algorithm analysis, and the data from the sensor was converted into context information. Then, fuzzy logic will use this information to control the liquid pump. The result can exhibit a high accuracy sensor with more than 0.9 of the coefficient determination, indicating that the sensor measurements perform similarly to the instrumentation devices as a reference. Moreover, the proposed system can successfully handle several conditions by utilizing the information processing of a context-aware algorithm and using it as control parameters of fuzzy logic.

Summary form only given. Interpolative reasoning plays a key role in both fuzzy logic and neural network theory. The basic approaches to interpolative reasoning in both fuzzy logic and neural networks were surveyed, and their differences and similarities were analyzed. An important issue in interpolative reasoning in fuzzy logic relates to the solution of a system of fuzzy algebraic equations. Various approaches to this problem, including fuzzy Lagrangian interpolation and the use of FA-Prolog, were described and analyzed. Among other issues discussed were the compression of a system of fuzzy if-then rules and the induction of rules from observations. >

Habitat suitability analysis is complex and requires integration of multiple attributes at a range of spatial scales. We use fine- and broad-scale variables within a spatial modelling framework to determine the suitability of greater sage-grouse nesting habitat using fuzzy logic. Fuzzy theory allows for a landscape component to have full, partial, or no membership in a particular management target. We used seven biophysical and anthropogenic variables correlated with nest site locations to build the fuzzy model. Using field and remotely sensed data and expert opinion, we developed fuzzy functions to define numerical membership values describing relationships between landscape characteristics and nesting habitat suitability. We used a fuzzy gamma overlay function to define five suitability classes in the resulting habitat map. Using a fuzzy logic model, we were able to spatially distinguish the suitability of nesting habitat using fine- and broad-scale attributes and characterise the uncertainty of the habitat model.

The first and second order lattices in fuzzy logics are identified as generalization of the first and second order lattices in two-valued logic. In this context, the language of fuzzy theory are restated in order to show the flexibility gained by the relaxation of basic axioms of the two-valued logic. Example of knowledge representation are presented with Type I and Type II fuzzy sets and logics together with second order semantic uncertainty information contained within the boundaries of Type II fuzzy sets.

Carcinogen risk exposure within manufacturing environments is a major occupational health risk which could have long-term implications for workers. The basic methods of monitoring carcinogen exposure include periodic inspections and periodic sampling, which cannot ensure real-time detection and prevention risk. This article presents a novel carcinogen risk assessment framework to combine IoT-enabled AI sensors with predictive technology to potentially address this issue. The framework proposed here would continuously monitor environmentally hazardous conditions in manufacturing settings where carcinogens exist. It would reliably detect whether harmful chemicals, particulate matter, or gases which could pose carcinogenic risks are present. The hazard prediction-program would use best practice machine learning (ML) models to predict exposure levels before they reach potentially hazardous levels, enabling workers to take preventative action in order to avoid the hazard altogether. Subscriber data-stream from IoT sensors that can detect hazardous gases or particulate matter will be accessed via edge AI modules for primary analysis, while cloud-based predictive modeling (i.e., long short-term memory (LSTM) networks and randomized forests) will advise them about the nature of future levels of exposure and exposures. This framework has empirical evidence demonstrating reasonable accuracy in detecting hazardous conditions and only takes a matter of seconds to deliver an alert. Upon notification of hazardous exposure, immediate real-time action is deployed, including cutting production, ventilation control, etc. Findings from simulations demonstrate the system’s ability to detect risk at over 96% accuracy and predict exposure events 2 hours in advance. This gives managers not only increased worker safety, but environmental health compliance as well. In the future, utilizing predictive modeling with IoT technology will represent a real leap forward in smart manufacturing, creating a scalable solution for managing carcinogen exposure risks and protecting worker health in industrial settings.

Despite advances in mining technology, the industry continues to grapple with ongoing issues of worker safety, human health, environmental impact, and low productivity. New 6G communication, AI, and IoT technologies are empowering the next generation of smart mining solutions through wearable devices that offer miners real-time monitoring, prediction, and immersion support. IoT sensors embedded in wearable devices—smart helmets, vests, etc.—constantly monitor the mining environment, equipment, and miner health. AI improves these smart wearables by automating the process of turning raw data from IoT sensors into intelligent information, helping to detect risks early, predict equipment maintenance, and balance miner workload. 6G provides ultra-reliable low-latency and high-capacity connectivity to support wearables even in the extreme underground conditions, while also enabling centimeter-level localization and holographic communication.

Nowadays, the utilization of IoT technology has been rapidly increased in various applications such as smart city, smart banking, smart transport, etc. The internet of things allows the user to collect the data easily using the different sensors installed at various locations in the open environment. The data collection process by the IoT sensors is giving access to the various services. However, due to the open communication medium, it is difficult to provide secure access to these services. In this paper, a data transmission technique has been proposed, which will provide secure communication in IoT infrastructure for smart city applications. In this method, each IoT sensor have to prove their legitimacy to the reader and the base station before the transmission of data. Hence, the IoT sensors can transmit the required data in a secure and efficient way. In the proposed technique, the proof of correction shows that the required information is not supposed to send through an online medium, it is obtained at the receiver using the Euclidean parameters shared by the IoT sensors. The proposed technique is compatible to provide the security against most of the attacks performed by the attackers. Two random variables and complex mathematical calculation are making the proposed technique more reliable than others. This technique will significantly improve the security of different data transmission services which will be helpful to improve the smart city infrastructure.

We describe in this paper a new approach for plant monitoring and diagnostics using type-2 fuzzy logic and fractal theory. The concept of the fractal dimension is used to measure the complexity of the time series of relevant variables for the process. A set of type-2 fuzzy rules is used to represent the knowledge for monitoring the process. In the type-2 fuzzy rules, the fractal dimension is used as a linguistic variable to help in recognizing specific patterns in the measured data. The fuzzy-fractal approach has been applied before in problems of financial time series prediction and for other types of problems, but now it is proposed to the monitoring of plants using type-2 fuzzy logic. We also compare the results of the type-2 fuzzy logic approach with the results of using only a traditional type-1 fuzzy logic approach. Experimental results show a significant improvement in the monitoring ability with the type-2 fuzzy logic approach.

These papers are originally published in the proceedings of Korea fuzzy logic and intelligent systems society (KFIS) fall conference in 1999. Eight papers are selected for this special issue. Major topics of them are fuzzy theory, neural network, inference system, intelligent controller, etc. In this issue, Seihwan Park and Hyung Lee-Kwang extend the concept of fuzzy hypergraph to type-2 fuzzy hypergraph using type-2 fuzzy sets. It has not only the same properties of hypergraphs but also the extended properties of them. It is also shown that interval valued fuzzy hypergraph is a special case of type-2 fuzzy hypergraph. Jung-Heum Yon, Yong-Taek Kim, Jae-Yong Seo and Hong-Tae Jeon design an efficient neural network called dynamic multidimensional wavelet neural network. It can perform an effective dynamic mapping with less dimensions of the input signal. These features show one way to compensate the weakness of the diagonal recurrent neural network and feedforward wavelet neural network. Yigon Kim, Yang Hee Jung and Young Chel Bae propose a new method for diagnosis of insulation aging using wavelet. It measures the partial discharge on-line from data acquisition system and analyses it using wavelet to acquire 21) patterns. They design a neuro-fuzzy model that diagnoses an electrical equipment using the data. Byung-Jae Choi, Seong-Woo Kwak and Byung Kook Kim develop an adaptive fuzzy logic controller. A sole input fuzzy variable is used to simplify the design procedure and the switching hyperplane of sliding mode control is used to improve the adaptability. Myung-Geun Chun, Keun-Chang Kwak and Jeong-Woong Ryu show an efficient fuzzy rule generation scheme for adaptive network-based fuzzy inference system using the conditional fuzzy c-means and fuzzy equalization methods. They apply this method to the truck backer-upper control and Box-Jenkins modeling problem. Daijin Kim proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Twostage classification method is used. All data are classified by using the lower approximation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. Min-Soeng Kim, Sun-Gi Hong and Ju-Jang Lee incorporate the Q-learning algorithm into the fuzzy logic controller. Modified fuzzy rule is used for the incorporation. As a result, a fuzzy logic controller is obtained that can learn through experience. Dong Hwa Kim designs a new 2-DOF PID controller and applies it to the operating data based transfer function of Gun-san Gas turbine in Korea. We hope that this issue can be helpful to readers and we appreciate professor Kaoru Hirota for his interest and support for the publication.

Since the invention of fuzzy sets and maturing of the fuzzy logic theory, fuzzy logic systems have been widely applied to various fields, such as fuzzy controller, data mining, and so on. New potential areas using fuzzy logic are also being explored with the emergence of other technologies. One booming technology today is the Internet, due to its fast growing number of users and rich contents. With huge data storage and speedy networks becoming available, multimedia contents like image, video, and audio are fast increasing. In order to search and index these media effectively, various content-based multimedia retrieval systems have been studied.