Critical rationalism faces difficulty in Karl Popper’s Socratic formulation: “I may be wrong, and you may be right, and by an effort, we may find the truth.” But the Socratic elenchus, using refutations, can only give us negative knowledge of general principles, which is not the wisdom we seek. Affirmatively, we can only find a collection of opinions to be coherent, which is one of many. Francis Bacon proposed an improved elenchus to find general truths. You must take up a limited topic to study, then cross-examine your evidence for and against its apparent nature. Experiments contrary to evidence and presumed knowledge are entered as self-contradictions in tables of opposition recorded in an “Experimental and natural history.” Such an account highlights a challenging puzzle if the account is to be made coherent. With enough problematized evidence, a coherent reading, or a solution of the puzzle, will be unique. Being both coherent and unique, it will be the truth about that limited reality being investigated. Unlike the method of hypothesis (“Anticipating Nature”), deciphering a coherent model is “Interpreting Nature,” allowing us to find a general truth on a limited topic. Isaac Newton achieved great success using Robert Boyle’s mechanistic version of this method. Using the “Experimental philosophy,” he discovered general principles of optics and astronomy.

Given n points continuously moving in 2d space, we want to maintain their convex hull each time. In this paper, first we use the kinetic data structure (KDS) framework and define a new KDS named Spiral. After that, we turn to both theoretical and Experimental evaluations of our KDS; in theoretical evaluation, consider the four quality measures of kinetic data structures in the worst case, but in the Experimental evaluation results of a simulation program are used to estimate the average case. We suggest an alternative efficiency parameter instead of previously defined and define a new responsiveness measure for the average case. The Experimental factors are much better than the theoretical worst case (this is especially true for the efficiency parameter; log2n instead of n.); hence conclude that we can’t reject a KDS with rather large theoretical worst case parameters. However, this study shows that when working with random positions, the worst cases used to evaluate a KDS aren’t always sufficient because these are rarely occurred and the expected average is so much better. In the worst case, from point of view of responsiveness, efficiency, locality and compactness our KDS belongs to O(n), O(n), O(C) and O(n) respectively (C is a constant number) and in the average case, these parameters for our KDS are O(log n), O(log2 n), O(C) and O(n) respectively. Note that previously, the two last parameters was O (log n) and O (n log n).

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic Algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic Algorithm and multi-objective optimization goals. The genetic Algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the Algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

In this paper, an alignment Algorithm is proposed based on using the outputs of inertial measurement unit (IMU). Accurate initialization of attitude angles containing the azimuth angle is an important step to increase the accuracy of inertial navigation system output of a moving vehicle. Two step alignment Algorithm including course and fine calculation is proposed in this paper which derives the Euler attitude angles including azimuth of vehicle with accuracy of a few arc minutes. First a course alignment is performed based on the components of gravity acceleration and angular velocity of the earth in body coordinate which are measured by inertial measurement unit sensors. Then the fine alignment is performed by constructing an error model of navigation system and estimation of the attitude angles error using a Kalman filter. The observations which are feed to the Kalman filter are the velocity components of vehicle in navigation frame and the eastern angular velocity component of the earth which are zero. To evaluate the accuracy of the proposed alignment Algorithm, the IMU is mounted on a 3DOF table which can be aligned with 5 arc second precision as reference attitude. Then the alignment Algorithm is executed in several azimuths. The results show the acceptable accuracy of azimuth angle.

In this paper, the uncertain dynamic parameters of an Experimental target tracker robot are identified through the application of genetic Algorithm. The considered serial robot is a two-degree-of-freedom dynamic system with two revolute joints in which damping coefficients and inertia terms are uncertain. First, dynamic equations governing the robot system are extracted and then, simulated numerically. Next, an open-loop experiment with finite duration step inputs is implemented on the Experimental setup to collect practical output data. Accordingly, a desired objective function is defined as the sum of discrepancy between the Experimental and simulated output data. Subsequently, a genetic Algorithm is employed to explore the best damping coefficients and inertia terms of the simulation scheme so as to minimize the presented cost function and taking into account the same input data for both simulation and experiment. Finally, the simulated output data based on the identified robot parameters reveal an acceptable agreement with the measured outputs through which validity of the identification scheme is affirmed.

Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle Swarm Optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle Swarm Optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

One of the basic topics in hydrological and river engineering studies is flood routing.Flood flooding is common in multi-tributary rivers and rivers without intermediate basin statistics. Therefore, to achieve the determination of slopes and cross-sections in all sections of the river, the Muskingum hydrological model is a useful method that helps to save information on the depth and flow of the flood at any time by saving time and money. To specify. In this study, the nonlinear parameters of the new Muskingum model are optimized based on the fly Algorithm (MA). In this non-linear model of Muskingum, which has eight parameters, the recovery coefficient γ is used, which has more or less values ​​than the number of peaks discharged in the output hydrograph.To evaluate the performance of Muskingum's new nonlinear model with the new MA Algorithm, the Wilson and Weisman-Lewis case study has been used by many previous researchers for validation.The results of the MA Algorithm for Wilson and Weissman-Lewis rivers show the minimization of the residual squares (SSQ) as the objective function, which is 3.21 for the Wilson River and 68722 for the Weissman River. The results of this study showed that the proposed model has high accuracy in estimating the output discharge values.

In this paper، using a new modeling، an Extended Kalman Filter (EKF) is presented for estimation of attitude (i. e. roll and pitch angles) and gyroscope sensor bias using a tri-axes acceleration and a tri-axes gyroscope. The Algorithm is developed for accurate estimation of attitude in dynamic conditions and existence of external body acceleration. The external body acceleration estimation as the main source of attitude estimation error in dynamic conditions is very important in attitude estimation accuracy، but in the literatures، the error of the external body acceleration on attitude estimation has not been studied in different dynamic conditions. The paper deals to estimation of the gyroscope sensor bias in two rotational axes (roll and pitch)، accurate attitude estimation in different dynamic conditions and estimation of external body acceleration. The proposed Algorithm application for attitude، external body acceleration and gyroscope sensor bias is evaluated by quasi-static and dynamic Experimental tests in high acceleration bound.

Purpose: In this paper, we consider the maximum traveling salesman problem, a variation of the usual traveling salesman problem, in which the objective is to maximize the cost of a tour of the salesman. The main purpose of this paper is to develop a hybrid genetic Algorithm (GA) for obtaining a heuristically optimal solution to the problem.Methods: First, a simple GA and then a hybrid GA have been proposed to solve the problem. As crossover operator plays a vital role in GAs, we modify the sequential constructive crossover operator for our simple GA to solve the problem. To improve the quality of the solution obtained by the crossover operator, restricted 2-opt search is applied. Then a hybrid GA is developed by incorporating a new local search Algorithm to the simple GA in order to obtain a heuristic solution to the problem.Results: We compare the efficiency of our hybrid GA against an existing heuristic Algorithm for symmetric traveling salesman problem library (TSPLIB) instances. Finally, we present solutions to the problem for asymmetric TSPLIB instances. Since, to the best of our knowledge, no literature presents solution for asymmetric instances, hence, we could not carry out any comparative study to show the efficiency of our hybrid GA for the asymmetric instances.Conclusions: The comparative study shows the effectiveness of our hybrid GA.