Fuzzy Logic Rules Research Articles

Online Self-Calibration of Multiple 2D LiDARs Using Line Features With Fuzzy Adaptive Covariance

The calibration techniques for a multi-sensor data fusion need to deal with the high accuracy of each sensor position to the reference frame. The point cloud data generated by low-cost LiDAR sensors possess high uncertainty, giving poor affection to the calibration system. Furthermore, the displacements between the sensors could be adjusted by undesirable situations during operation time. This paper proposes an automatic calibration framework for multiple low-cost 2D lasers on the special Euclidean group SE(2). The key ideas of the proposed method are to perform the line feature-based factor graph optimization and fuzzy inference system to adapt the covariance of the factor graph. Here, to fuse the line feature information, we mimic the weight affection technique using the fuzzy logic rules and then add the adapted covariance to the graph. Next, we suggest an online self-calibration algorithm running in real-time to correct the variation of calibrated parameters. Lastly, the proposed calibration system is evaluated through a real-world dataset. The experiment results show that the proposed method can be widely applied for industrial applications.

A Novel Self-Tuning Fuzzy Logic-Based PID Controllers for Two-Axis Gimbal Stabilization in a Missile Seeker

Tracking a target is an essential function of a seeker for missiles. The target tracking mechanism of a seeker consists of gimbals, mounted with gyroscopes, and an antenna or some other energy receiving devices such as radar, infrared (IR), or laser. Stabilization of such a gimbal is necessary for any guided missile to maintain the tracking device always pointing towards the target. For the stabilization of the gimbal system, several control methods have been employed for making the gimbal to follow an input rate command by eliminating all the gimbal disturbances. Here, a new self-tuning fuzzy logic-based proportional, integral, derivative (PID) controller is introduced for the stabilization of a two-axis gimbal for a manoeuvring guided missile. The proposed control method involves tuning the gains of the PID controller based on the fuzzy logic rule bases considering the missile body rotation. The performance of the stabilization loops has been verified through MATLAB simulations for fuzzy logic-based PID controller compared with the conventional PID controller. The simulation results show the response of the gimbal system with stabilization loops met the control requirements with fuzzy PID controllers but not with conventional PID controllers.

Fuzzy Logic Control System for Fuel Mixture Quality in Spark Ignition Engines.(Dept.M)

Spark ignition engine performance and exhaust emissions are strongly affected by the fuel/air ratio used. A considerable amount of investigations have been carried out by engine manufacturers to control engine performance and meet exhaust emission standards of governmental agencies by developing control systems for some operating variables such as ignition timing(IT), Fuel Air Ratio(FAR), and Exhaust Gas Recirculation (EGR) This paper presents a fuzzy control system that measures exhaust temperature and operates a needle valve on the engine earburetor jet that enables the fuel to air ratio lo oscillate about a desired value. A group of experiments have been carried out to obtain the relationship between fuel to air ratio or (ɸ), Exhaust temperature and exhaust emissions. A group of fuzzy logic rules which relate exhaust temperature, to the desired speed with the position of a needle valve actuated by a stepping motor are prop osed. A beneficial of fuzzy control method was that the rules determined the characteristics of control system are interpreted by the operator. Experimental and simulated results show that the prop osed fuzzy system improves the fuel/air mixture quality.

Experimental investigation and optimal combustion control of untreated landfill gas via fuzzy logic rule knowledge based approach

Optimal combustion control of untreated landfill gas is proposed for an effective usage and a low-cost solution in waste to energy technologies. Variations of methane concentration in untreated landfill gas over time cause undesired performance of combustors in thermal efficiency and gas emission. In this work, the experimental investigation on variables of combustion process is systematically presented to determine an inherent performance index of a combustor, reflecting actual thermal efficiency and gas emission for optimal control. Those quantitative findings can be implemented via fuzzy logic rule knowledge based approach to combustion control. From the experimental results of a can-type combustor, it is confirmed that the optimal combustion control of untreated landfill gas yields the desired exhaust gas temperature with maximum thermal efficiency and minimum gas emission under varying methane concentrations or changes of operating conditions. The proposed methodology can be generalized to optimal combustion control of untreated landfill gas via the fuzzy logic rule knowledge based approach, which is the most suitable to characteristics of each combustor. For industrial applications, the experimental results at a laboratory scale can be extended by increasing the capacity of the studied combustor to obtain the large scale combustion in practical production where the optimal combustion control of the untreated landfill gas is effectively accomplished without cost of gas treatment for usage in conventional processes.

Mitigating Traffic Congestion at Road Junction using Fuzzy Logic

The timing of traffic lights at intersections is determined by the Local Authority. It is based on the peak hour statistics and the timing is maintained even during an off-peak hour. This one standard green time will be used every time in a day regardless of the number of vehicles and the road width. This approach will have a long green traffic light even though the number of vehicles is only a few and hence, will cause a long waiting time at the intersections. Therefore, the aim of this study is to vary the timing of traffic lights at junctions according to the number of vehicles. This paper will also consider road width variable which have not been considered so far. Fuzzy logic rules will be used to classify the number of vehicles and road width and time taken for vehicles to move at the intersections which was proposed in a previous work. The new timing will commensurate with the number of vehicles and road width. Field test data were gathered from Sala Benda and Semplak intersections which are amongst the busiest intersections in Bogor, Indonesia. Comparisons were made and show that the green light timing obtained is appropriate to the two factors considered. Also the waiting time for vehicles in each traffic cycle was also reduced. This study have formulated an optimal green lights timing in each intersection and will be used by local authorities to determine the timing of green traffic lights at the intersection and hence, can implement traffic control and an appropriate waiting time.

Super Resolution of MR Brain Images Using Compressive Sensing and Fuzzy Logical Rules

HIGHLIGHTS In medical surround, high resolution images are needed to discern the details of image. Super resolution image can be made from one/few images by means of compressive sensing. A reckoning soft computing way for resolution enhancement, fuzzy logic is implemented. Better results are obtained for a dataset with different performance metrics.

Green supplier selection using fuzzy Delphi method for developing sustainable supply chain

The objective of this paper is to examine the determinants of the supplier selection process with green consideration. Thus, this analysis gathers a collection of factors from established literature of green supplier selection (GSS), including seven categories and 58 attributes. The objective of this research is to classify the key factors which are presented as qualitative information. Fuzzy logic rules are used to transform qualitative expert knowledge into numerical data. Then, we adopt the Delphi method (DM) to filter and rate unneeded factors according to their relevance. The results indicate 24 important factors for the GSS process. Five categories are included: Performance and technology ability, Environmental management, Pollution control, Quality and Service. The most significant factors are recognized as green research and development, eco-design, green image, green packaging and remanufacturing. Finally, the debate is held on the basis of the findings and future research are also recognized and stated.

Визначення релевантності інформації, отриманої від пошуково-довідкового сервісу на веб-платформі.

The article analyzes some possibilities of a quick determination of the relevance of information obtained from some search and reference services located on various web platforms. An important feature to ensure the survivability of the information on the Internet for reference and search services has been identified. This feature consists in providing services to an array of sample data to verify information obtained from primary sources or through the mechanism of following links, which confirm the information or provide an answer to the request in case of failure. It is proposed to consider a search service or some reference platforms using the production rules of if-THEN structures, depending on the available facts. An indicator of the effectiveness of the formalized information is derived through a determinant of the level of the information error when processing it under the conditions of uncertainty, providing that the determinant highlights the data that is not enough to describe the object of the re-quest, and that there may be a solution to system problems among the data. Its representation through some estimated values is given. When developing a search or reference service on a web platform using the proposed approach, such services will hold not just arrays of information structured by topic, but formed arrays of information based on symbolic transformations using both general and separate schemes for displaying information on the request, as well as with the ability to reformulate tasks and queries with the aim of getting the most complete and different information on the request. A mecha-nism for detecting truth and falsity when receiving responses to the request from a service according to the rules of fuzzy logic, when understanding the set can be replaced by understanding the characteristic function, is proposed. An example of an algorithmic implementation is provided as well. All stated above can be used to optimize reference and search services on web platforms, improve the work of search engines and various distributed information processing services.

WEDM process optimization of sintered structural ceramic sample by using fuzzy-MPCI technique

Abstract Sintered boron carbide is an extremely hard, structural ceramic material and it is difficult to be machined with conventional techniques. To overcome the machining problem, the spark plasma sintered (SPS) monolithic boron carbide (B4C) was successfully machined by wire electrical discharge machining (WEDM) as the material is electrically conductive. The effects of five different machining parameters of WEDM were carefully observed to check their effect on the useful responses, namely machining speed and surface roughness (Ra) for cutting sintered B4C samples. A number of experimental operations were derived by using the concept of central composite design (CCD) and fuzzy logic was implemented to predict the response for a particular input parameter set. Also, a multi objective optimization was performed by fuzzy logic rule based multi performance characteristics indices technique (MPCI).

Process abnormity identification by fuzzy logic rules and expert estimated thresholds derived certainty factor

Abnormity identification plays a significant role in keeping a process healthy and safe. The rule-based expert system (RB-ES) is an efficient approach in this field. However, the relationship between propositions is fuzzy and uncertain when the process knowledge is summarized into rules. Moreover, the process data utilized as observed evidence to activate the uncertain rules face the imprecise measurement problem. The imprecise measurement problem comes from noises, heavy dust, grease pollution, and other circumstance factors that influence sensors. To address the above issues, an abnormity identification framework based on fuzzy logic rules is proposed in this work. Two kinds of uncertain rules with fuzzy linguistic information and expert estimated threshold in premises are specially considered. First, a deviation estimation method based on probability distribution is proposed to extend the imprecise data into the interval data to solve the uncertain problem caused by imprecise measurement. Second, the fuzzy piecewise membership function is utilized to calculate the certainty factor (CF) of the premise in rules, which describes the occurrence risk of an event or abnormity. Third, the inference steps based on the CF expert system with uncertain rules and imprecise activated variables are presented to identify the abnormity in the complex industrial processes. The proposed method is applied to the thickening process in hydrometallurgy and performs better than the traditional rule-based method and the PLS method on the actual process data. The accuracy of the proposed method still maintains higher when more noises are added, generally 8%–10% higher than the other two methods. The proposed method also shows advantages in identifying multi-abnormity and reducing model establishing workload.

Detection of attacks in the corporate network using the rules of fuzzy logic

Detection of attacks in the corporate network using the rules of fuzzy logic

On the Road: Route Proposal from Radar Self-Supervised by Fuzzy LiDAR Traversability

This is motivated by a requirement for robust, autonomy-enabling scene understanding in unknown environments. In the method proposed in this paper, discriminative machine-learning approaches are applied to infer traversability and predict routes from Frequency-Modulated Contunuous-Wave (FMCV) radar frames. Firstly, using geometric features extracted from LiDAR point clouds as inputs to a fuzzy-logic rule set, traversability pseudo-labels are assigned to radar frames from which weak supervision is applied to learn traversability from radar. Secondly, routes through the scanned environment can be predicted after they are learned from the odometry traces arising from traversals demonstrated by the autonomous vehicle (AV). In conjunction, therefore, a model pretrained for traversability prediction is used to enhance the performance of the route proposal architecture. Experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community. Our key finding is that joint learning of traversability and demonstrated routes lends itself best to a model which understands where the vehicle should feasibly drive. We show that the traversability characteristics can be recovered satisfactorily, so that this recovered representation can be used in optimal path planning, and that an end-to-end formulation including both traversability feature extraction and routes learned by expert demonstration recovers smooth, drivable paths that are comprehensive in their coverage of the underlying road network. We conclude that the proposed system will find use in enabling mapless vehicle autonomy in extreme environments.

A Step Length Estimation Model of Coefficient Self-Determined Based on Peak-Valley Detection

Without any preinstalled infrastructure, pedestrian dead reckoning (PDR) is a promising indoor positioning technology for pedestrians carrying portable devices to navigate. Step detection and step length estimation (SLE) are two essential components for the pedestrian navigation based on PDR. To solve the overcounting problem, this study proposes a peak-valley detection method, which can remove the abnormal values effectively. The current step length models mostly depend on individual parameters that need to be predetermined for different users. Based on fuzzy logic (FL), we establish a rule base that can adjust the coefficient in the Weinberg model adaptively for every detected step of various human shapes walking. Specifically, to determine the FL rule base, we collect user acceleration data from 10 volunteers walking under the combination of diverse step length and stride frequency, and each one walks 49 times at all. The experimental results demonstrate that our proposed method adapts to different kinds of persons walking at various step velocities. Peak-valley detection can achieve an average accuracy of 99.77% during 500 steps of free walking. Besides, the average errors of 5 testers are all less than 4 m per 100 m and the smallest one is 1.74 m per 100 m using our coefficient self-determined step length estimation model.

System for assessing the sustainability conditions of small hydro plants by fuzzy logic

AbstractThis work presents a system for an integrated evaluation of the implementation of small hydroelectric power plants (SHPs), based on a soft decision tree. The method generates the enterprise's sustainability index, where economic, environmental, and social aspects are considered in its interactions, with indicators measured in the field, sectoral indexes, and final index generated by fuzzy logic rule blocks. The methodology used to select the indicators and indexes followed four phases. First, a fuzzy expert system (FES) was developed in the form of a soft decision tree. Then, the array of suggested variables and weights were submitted to expert analysis using the Delphi methodology, and their assessments were collected. The third phase was the improvement of the system, taking into account the obtained considerations. In the fourth phase, the system was applied to a case study based on the installation of an SHP in the southern region of Brazil. The results obtained in the case study showed the sustainability of the enterprise. The presented method can have a significant contribution in supporting decisions regarding the licensing of hydroelectric projects.

Adaptive optimal fuzzy logic based energy management in multi-energy microgrid considering operational uncertainties

The intelligent decision making of multi-energy management in a microgrid is a non-trivial task due to the intermittent and stochastic nature of highly penetrated renewable energy sources and demand. To address such a challenge, the energy management system often adopts the prediction based day-ahead energy scheduling and real-time energy dispatch to optimally coordinate the operation of dispatchable components, e.g., battery-based energy storage and thermal units. This paper presents an adaptive optimal fuzzy logic based energy management solution to develop appropriate day-ahead fuzzy rules for real-time energy dispatch adaptively in the presence of operational uncertainties. The solution determines the optimal fuzzy inference system (e.g., the membership function shape and the inference rules set) based on the predicted information over a certain period through a novel offline meta-heuristic optimization algorithm. The real-time energy dispatch based on the obtained optimal fuzzy logic rules can be further carried out to meet the various operational criteria, e.g., minimal power fluctuation and operational cost. The proposed solution is extensively evaluated through simulation experiments in comparison with two existing approaches: the online rule-based dispatch method and the meta-heuristic optimization-based offline scheduling method. The numerical results demonstrate the superior performance of the proposed energy management solution.

Iterative-learning error compensation for autonomous parking of mobile manipulator in harsh industrial environment

In wide-area and multi-sites manufacturing scenarios, the mobile manipulator suffers from inadequate autonomous parking performance due to the harsh industrial environment. Instead of struggling to model various errors or calibrate multiple sensors, this paper resolves the above challenge by proposing an iterative-learning error compensation scheme that consists of offline pre-regulation and online compensation, which can improve the compensation efficiency and accommodate the error fluctuations caused by environmental fluctuations. Integrating an improved Monte-Carlo localization and eye-in-hand vision technique, an effective measurement system is firstly developed to accurately obtain the parking data without requiring superfluous facilities or cumbersome measurement. Then, after removing the data outliers utilizing the Grubbs test, offline pre-regulation is achieved to give a suitable initial value and increase the compensation convergence. To reduce the time-varying systematic errors and parking error fluctuations, online compensation is presented by offering an efficacious estimation of environmental fluctuations using fuzzy logic rules and providing an adaptive iterative-learning law. Finally, the feasibility and effectiveness of the presented compensation method are validated by extensive experiments implemented on a self-developed mobile manipulator.

Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic

The use of natural language processing (NLP) methods and their application to developing conversational systems for health diagnosis increases patients’ access to medical knowledge. In this study, a chatbot service was developed for the Covenant University Doctor (CUDoctor) telehealth system based on fuzzy logic rules and fuzzy inference. The service focuses on assessing the symptoms of tropical diseases in Nigeria. Telegram Bot Application Programming Interface (API) was used to create the interconnection between the chatbot and the system, while Twilio API was used for interconnectivity between the system and a short messaging service (SMS) subscriber. The service uses the knowledge base consisting of known facts on diseases and symptoms acquired from medical ontologies. A fuzzy support vector machine (SVM) is used to effectively predict the disease based on the symptoms inputted. The inputs of the users are recognized by NLP and are forwarded to the CUDoctor for decision support. Finally, a notification message displaying the end of the diagnosis process is sent to the user. The result is a medical diagnosis system which provides a personalized diagnosis utilizing self-input from users to effectively diagnose diseases. The usability of the developed system was evaluated using the system usability scale (SUS), yielding a mean SUS score of 80.4, which indicates the overall positive evaluation.

Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation

Global warming represents a serious challenge, which requires the adoption of renewable energy technologies worldwide. However, it can negatively affect the availability of renewable energy resources, such as wind, which are needed for electricity generation. In this context, there is an increasing need for more accurate evaluations of wind turbine power curves. A novel methodology to model the power curves of wind turbines, which combines the use of artificial neural networks (ANN) and Fuzzy logic rules, is proposed in this paper. This methodology assesses the role of environmental temperature in the power curve and the impact of temperature increases on wind energy production. The application of this methodology is illustrated with the simulation of the impact of global warming on the electricity generation of a wind farm. Due to the non-linear relationship between the power output of a turbine and its primary and derived parameters, it is shown that ANN combined with an expert system formed by a Fuzzy logic module fit power curve modeling processes well. The application of the methodology shows that an increase in temperatures would trigger a small reduction in the performance of wind turbines.

Improving Text Summarization using Ensembled Approach based on Fuzzy with LSTM

Abstractive text summarization using attentional recurrent neural network (sequence-to-sequence) models have proven to be very effective. In this paper, a novel hybrid approach is presented for generating abstractive text summaries by combining fuzzy logic rules (which selects extractive sentences) with bidirectional long short-term memory (Bi-LSTM) which further produces abstractive summary. Bi-LSTM uses attention mechanism and Adam optimizer for updating network weights. The proposed approach utilizes fuzzy measures and inference to extract textual information from the document to find the most relevant sentences. These relevant sentences are given as input to Bi-LSTM to produce an abstractive summary of the significant sentences. The proposed FLSTM model is evaluated using ROUGE toolkit. The experiment is performed on standard datasets (i.e., DUC and CNN/daily mail). Another salient feature of this work is merging of DUC 2003–2004, DUC 2006–2007 datasets to generate a larger dataset to achieve better results. The FLSTM model is compared with other state-of-the-art models, and the empirical results suggested that the proposed FLSTM model outperforms all other models.

Intelligent privacy preservation electronic health record framework using soft computing

With the recent advancements in the technology, the biggest challenges are to protect the important sensitive data of electronic health records that are available over web. The real issues on EHRs to hide the sensitive huge data especially stored in distributed environment, and shared between numbers of stakeholders. It is an essential condition to eliminate the superfluous data and maintains the privacy of individual record stored in EHRs. An efficient mechanism is required to apply for privacy preservation on EHRs before it is disclosed to the public domain. The perturbation techniques are used to hide the data from unauthorized person for the security and privacy of a patient’s personal and sensitive information in electronic health records system. In this paper we propose a privacy preservation of electronic health records (PPEHR) framework to hide sensitive information using combination of fuzzy logic and perturbation association rules. In this framework, the privacy preservation is being tested using soft computing. Experimental results are demonstrated and verified with the help of machine learning algorithms.