Fuzzy controlled robot arm playing two-dimensional ping-pong game

Wood is a natural derivative, it possesses randomly distributed inherent defects all over its mass. This complicates the cost estimation process; data collected showed that even planks with similar defect pattern would have different percentage of material loss. Such uncertain loss was caused by changes in cutting parameters. In this study, a Fuzzy Inference (FI) method was employed to predict wood loss in a rubber wooden toy manufacturing cutting process. Notable variables are: length of cut, and area of cut. Prediction accuracy from the FI method was compared with that of other alternative methods. Common practice assumes a constant defective proportion, resulting in inaccurate cost estimation. A regression equation allows the loss to be varied by the cutting parameters; but only one parameter was found to be significant. Experimental results show that the FI method greatly outperformed regression and conventional methods. These findings emphasize the influence of cutting parameters on product cost. Accurate cost estimation enables better planning for efficient pricing strategies and enhances business competitiveness.

Water management has become a challenge and a key factor to achieve proper field management in a brownfield in the Amazon region. Apart from historically increasing the basic sediments and water (BSW) and flow assurance events, the longevity, ullage, and lack of maintenance of the processing facilities have come key conditions to sustain production and reduce lifting costs. An integrated digital solution was implemented to enable predictive analysis and behavior of the water system at the facilities level, such as horizontal pump system (HPS) failures, line plugging, injection rate deviations, as well as forecasting of injection rates in real time to improve the efficiency of operations and avoid production deferment. Several failures had occurred in the water-handling system caused by the lack of real-time monitoring or fast event detection and corrective actions. This has led to many shut-ins and failures of electric submersible pumps (ESPs), and injectivity losses in the waterflooding and disposal wells, triggering production losses. Hardware for data collection in selected points and customized digital advanced workflows using data analytics and machine learning (ML) were implemented and developed to optimize processes and production. Therefore, using the connectivity provided by a satellite system, supervisory control, and data acquisition (SCADA) optical fiber and operations monitoring platform, the variables are now monitored in real time to enable early identification of events, give a rapid response, and optimize production of the field. The digital solution was implemented using the following steps: Data capturing: Data gathering was improved by adding sensors at key points, aiming to minimize data gaps in the field, and ensuring comprehensive availability in the database. Surveillance workflows: Multiple workflows were created using edge computing and ML to have an early alert and recommendations to make a rapid response. Opportunity identification: Workflows were implemented in real time, enabling early identification of events in the field to give a rapid response, debottlenecking, and optimize production by proper water-handling management through data shown in engineering dashboards. This innovative and integrated digital solution has shown outstanding results. Monitoring the data from the water-handling system in real time and applying engineering workflows has led to a 76% reduction in processing time, 75% reduction in commuting for data gathering, and 5 t of carbon dioxide (CO2) reduction per year. Due to the early event identification, the prediction of potential failures, and a rapid response time, the operational team has reduced deferred production by 283,000 bbl of oil, saved USD 9 million related to HPS failure reduction and saved production, which contributed to extending the ESP's run life (RF), optimizing maintenance costs, and reducing lifting costs. In this paper we outline how the implementation of a novel digital solution can optimize the process, production, and cost of the critical points for a brownfield by using data analytics, edge computing, and ML, potentiating the digital transformation path in all stages of the energy industry.

This main purpose of this paper is to promote the efficiency of a control system using a scheduling policies control design. In this system, the management of a computer’s input and output information is handled appropriately by the program language. The scheduling policies control design is used in the robotic arm’s tracking system. The advantage of this control design is to activate each procedure running simultaneously when the transient overload of the information’s input and output in the control system occurs. Therefore, the time run in the scheduling policies control system will be shorter than that of a traditional control system in which each procedure is lined up for running. In this paper, case studies of the scheduling police control application used in image tracking vision control are introduced. The results reveal that the speed of the tracking system can be improved by using the scheduling police technique under an immediate procedure plan.

Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have applied those models in a multiprocessor environment. Few, however, have recognized the inefficiencies associated with the underlying spatial modeling framework used to implement such analyses. In this paper, we identify a common inefficiency in processing spatial models and demonstrate a novel approach to address it using lazy evaluation techniques. Furthermore, we introduce a new coding library that integrates Accord.NET and ALGLIB numeric libraries and uses lazy evaluation to facilitate a wide range of spatial, statistical, and machine learning procedures within a new GIS modeling framework called function modeling. Results from simulations show a 64.3% reduction in processing time and an 84.4% reduction in storage space attributable to function modeling. In an applied case study, this translated to a reduction in processing time from 2247 h to 488 h and a reduction is storage space from 152 terabytes to 913 gigabytes.

Attrition milling is an easily scalable and environmentally friendly process used to produce Ca3Co4O9+δ nanoprecursors in a relatively short time. Sintered materials produced through the classical solid-state method, involving ball milling, show much larger grain sizes and slightly lower density than those obtained in samples produced from attrition-milled precursors. On the other hand, electrical resistivity has been drastically decreased, accompanied with a slight decrease in the Seebeck coefficient in samples obtained from these attrition-milled precursors. Moreover, the use of an attrition milling process leads to a very important reduction in processing time (around 75%), together with a slight power factor improvement of around 10%, when compared to the classically prepared samples.

This paper presents an experimental of fuzzy logic with Mamdani's fuzzy inference method. This experimental applied to microcontroller using the C programming. So far, fuzzy logic with Mamdani's fuzzy inference method many applied only at the level of simulation. The experiment was intended to compare the simulation matlab with microcontroller programming. In programming the microcontroller used a methodology that can produce the same simulation results. With this experiment is expected to help the programmer microcontroller to implement fuzzy logic with Mamdani method. And this method is expected to be applied to the control system in the field of electronics that require fuzzy logic system.

Fuzzy inference has a multigranular architecture consisting of symbols and continuous values, and has worked well to incorporate experts' know-how into fuzzy controls. Stability analysis of fuzzy control systems is one of the main topics of fuzzy control. A recently proposed stability analysis method on the symbolic level opened the door to the design of stable fuzzy controller using symbols. However the validity of the stability analysis in the symbolic system is not guaranteed in the continuous system. To guarantee this validity, a nonseparate condition has been introduced. If the fuzzy control system is asymptotically stable in the symbolic system and the system satisfies the nonseparate condition, the continuous system is also asymptotically stable. However this condition is too conservative. The new condition called a relaxed nonseparate condition has been proposed and the class of control systems with guaranteed discretization has been expanded. However the relaxed condition has been applicable only to controf systems having symmetric membership functions. This paper presents a new fuzzy inference method that makes the relaxed condition applicable to fuzzy control systems with asymmetric membership functions. Simulations are done to demonstrate the effectiveness of the new fuzzy inference method. The proof of the expansion of the relaxed nonseparate condition is also given.

More and more records or charts of historical financial data are used for technical analysis, hoping to identify patterns that can be exploited to achieve excess profits. Technical analysis has been widely used in the real stock market to forecast stock price or stock trading points. The good association of technical indicators can obtain good prediction results in stock markets. But the selection of technical indicators is also a tough problem. In this paper, we introduce a forecasting model incorporating biclustering algorithm with a new fuzzy inference method. Biclustering algorithm discover biclusters which are regarded as trading patterns. And a new fuzzy inference method is used for determining trading points. The proposed forecasting model (called BM-FM) was used for predicting three real-world stock data. The experiment is designed by comparing the profit ratio in TPP-based strategy, IPLR and IPLR-ANN with the profit ratio in our forecasting model. According to experimental results, it is indicated that our model obtains more earnings and higher profit ratio than other comparative methods.

The blood pressure signal is weak nonstationary biomedicine signal. Wavelet analysis is used to pick up the character of blood pressure signal for useful information and the truth of the oscillation amplitude of artery oscillation curve is reckoned by fuzzy inference method. Sequentially, the exact relation between the cuff pressure and the oscillation amplitude is developed and this relation is fitted with recursive least square method. Finally the systolic pressure and the diastolic pressure are calculated with the wave amplitude threshold method. With wavelet analysis and fuzzy inference method, the interference resulted from cardiovascular disease or movement on measuring is effectively decreased and the accuracy of blood pressure measurement is improved

This paper describes our 1.06um pulsed laser source which has the capability to generate pulses with tunable durations from picoseconds to nanoseconds. This is made possible through the use of a high speed Field Programmable Gate Array (FPGA) board to generate the required pulses/ any arbitrary waveforms. The FPGA codes are specially designed to generate very fast rise/fall times waveforms with the flexibility to vary both the pulse duration and pulse frequency in real time. These custom defined waveforms are then used to generate high intensity laser pulses when connected to a 1064nm fiber laser source. These tunable pulsed laser sources have potential industrial applications in precision laser micro machining, where the high thermal intensity of the laser pulses can be used to perform ablation, cutting, drilling or annealing of processed materials. Furthermore, by able to tailor the shape of the delivered laser pulses, this will lead to reduction in processing time, improves the efficiency of material removal and the quality of the end product. Our custom board can generate arbitrary tailored pulses as short as 200ps and the longest pulse is 200ns. The pulse repetition rate can be tuned from 20 kHz to 1MHz.

The rule representation opens a new dimension for expressing changes of fuzziness in fuzzy rule-based systems. In the case of standard Fuzzy rule representations, it is difficult to describe fuzzy functions in which crisp observations are required to have fuzzy conclusions, or in which an increase in the fuzziness of observations leads to reduced fuzziness in conclusions. These problems are mainly due to a lack of information. A fuzzy point rule determines the connection between a pair of fuzzy sets taken from the domain and the range of the rule. Expressing the fuzzy function through a set of fuzzy points and fuzzy interpolation between pairs of those points, each fuzzy point can be considered as a node point with given location and fuzziness. In common, sparse rule-base definitions, these node points are usually disjunctive on the domain, defining only single antecedent-consequent fuzziness connections at the location of the fuzzy points. However, this kind of information is insufficient when the goal is to express changes in the fuzziness of a given location in the domain. One solution to this problem is the double fuzzy point rule representation concept. Double fuzzy points are pairs of fuzzy points which share the same reference locations, but have different fuzziness properties. The existence of two different fuzziness values in a single location within the domain creates new possibilities for introducing fuzzy interpolation methods capable of interpolating not only between locations, but between changes in local fuzziness values as well. The main goal of this paper is to discuss how two-step Rule Interpolation methods can be adapted to be able to handle the double fuzzy point concept. To this end, an approach referred to as the Generalized Double Point Methodology (GDFPM) is proposed.

The attributes in database are very important information. However, sometimes they cannot be applied efficiently. What we concern about is how to make use of them as much as possible. AFS theory is a new analytic method of fuzzy mathematics. The membership functions of AFS theory are obtained by a uniform arithmetic according to the original data. AFS theory and closeness degree functions are combined to analyze the relation of the attributes in database. Using AFS theory and fuzzy inference methods, a new fuzzy inference method is proposed. At the same time, we get a method of attribute reduction. The results are very similar to human intuition and show that the methods are convenient for data mining and attributes analyzing.

This paper presents fuzzy control method (Mamdani’s fuzzy inference method) for maximum power point tracking (MPPT) of photovoltaic (PV) system under varying irradiation and temperature conditions. The fuzzy control method has been compared with perturb and observe (P&O) method as one of the most widely conventional method used in this area. The different techniques have been analyzed and simulated. Fuzzy technique gives better and more reliable control for this application.KeywordsPhotovoltaicPerturb and observFuzzy controlMPPT

Three groups of children participated in a referential communication task to assess ability to respond to ambiguous and unambiguous instructions. Results indicated that language-impaired children used less clarification requests than normal children of the same chronological age when faced with ambiguous information. However, performance of the language-impaired children was not different from a younger, language-age matched group.

Due to the expanding trend of smart homes, electromobiles and renewable and alternative energy sources, there is the possibility of linking these systems. In order to optimize and interconnect these systems, all input and output information from these systems must be known. The define main and the secondary optimization criteria and then design the control system according to this optimization criterion. This master control system must be able to work with all on-line inputs and outputs information in real-time, as well as with the prediction and statistics of already accumulated data.