Self-adjusting Algorithm Research Articles

Dependence of objective function on several variables in layout problem and its solution by the method of structural-alphabetic search

The problem of the layout of objects of different sizes is analyzed. The objective function is shown to depend on several variables, which are various combinatorial configurations. According to this principle, the problem is divided into several subproblems; therefore, a self-adjusting algorithm is developed to solve it. A location problem for objects of the same size is solved by the structural-alphabetic search, which is based on the known solvable case.

2P1-I02 タスクにおける群ロボットの適応的グループ生成手法(ネットワークロボティクス)

This paper addresses the task-oriented group generation problem for performing cooperative tasks by robot swarms. In detail, given task conditions such as the number of robot required, how can a swarm of heterogeneous robots create individual groups corresponding to desired tasks through consensus? As the solution approach, we propose a decentralized task-oriented group generation strategy, which is composed of group consensus and self-adjustment algorithms. The group consensus algorithm enables robots to select a leader robot and generate each group based on the leader. Through the self-adjustment algorithm, to meet assigned task conditions, the leader attempts to recruit more members or any robots are dismissed against the group. By doing this, robots can organize their group according to the task conditions. Extensive simulations are performed to verify that the proposed algorithms effect a computational efficient and adjustable self-organization.

Research and Verification of the Logic Threshold Self-adjusting Control Method for ABS

Independently developing anti-lock braking system(ABS) is the basis of mastering the active dynamic control technology of vehicle chassis.According to the experimental data and the logic threshold control method widely used in the industrial ABS,the impact on ABS control of every logic threshold is analyzed and the basic rules of calibrating the logic thresholds is established.By borrowing the idea from the self-tuning regulator in adaptive control,a logic threshold self-adjusting algorithm is presented.The algorithm identifies the optimal brake slip ratio of the road and calculates the reference speed on-line.The main logic thresholds are adjusted automatically according to the error between the wheel slip ratio and the optimal slip ratio in order to improve the control performance.The control parameters are calibrated and the control performance of the self-adjusting algorithm is verified by road tests under various conditions.The test results indicate that the presented logic threshold self-adjusting algorithm overcomes the influence of different factors,such as road adhesion coefficient,speed and load,on the ABS,thereby the optimal ABS control under every condition is realized.The developed ABS has good control effect and meets the development requirement.It provides a foundation for ABS industrialization.

Digital ratemeter with fast response

Digital ratemeter that operates according to a self-adjusting algorithm, which at low rates automatically switches from a preset count to the preset time method, is proposed. It combines the good properties of both methods concerning the accuracy of the measured average counting rate and the response time to sudden changes of rate.

Simultaneous frequency doubling and polarization coupling in aperiodically poled lithium niobate

We proposed a method to simultaneously implement quasi-phase-matching (QPM) second harmonic generation (SHG) and polarization coupling for arbitrary wavelengths in an aperiodically poled lithium niobate (APPLN). It allows for modulation of both polarization and magnitude of the SHG light by an external electric field. A method called the self-adjusting algorithm (SA) is suggested for designing the aperiodic microstructure. The numerical simulation shows that it is the competition between the SHG and polarization coupling processes, which is dominated by the external electric field, that makes the output tunable.

Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors.

The development of intelligent sensors involves the design of reconfigurable systems capable of working with different input sensors signals. Reconfigurable systems should expend the least possible amount of time readjusting. A self-adjustment algorithm for intelligent sensors should be able to fix major problems such as offset, variation of gain and lack of linearity with good accuracy. This paper shows the performance of a progressive polynomial algorithm utilizing different grades of relative nonlinearity of an output sensor signal. It also presents an improvement to this algorithm which obtains an optimal response with minimum nonlinearity error, based on the number and selection sequence of the readjust points. In order to verify the potential of this proposed criterion, a temperature measurement system was designed. The system is based on a thermistor which presents one of the worst nonlinearity behaviors. The application of the proposed improved method in this system showed that an adequate sequence of the adjustment points yields to the minimum nonlinearity error. In realistic applications, by knowing the grade of relative nonlinearity of a sensor, the number of readjustment points can be determined using the proposed method in order to obtain the desired nonlinearity error. This will impact on readjustment methodologies and their associated factors like time and cost.

Algorithm to design aperiodic optical superlattice for multiple quasi-phase matching

We report a new algorithm, called the self-adjusting algorithm, to construct an aperiodic optical superlattice in which multiple nonlinear optical parametric processes can be realized simultaneously with high conversion efficiency. The numerical simulations show that a self-adjusting algorithm has obvious advantages that are due to its own physical process and feedback function. Especially in comparison with other existing algorithms, a self-adjusting algorithm can eliminate the need to search blindly and is independent of the initial conditions.

A Self-Adjusting Simple Hyperstable Adaptive Recursive Algorithm

Adaptive infinite impulse response (IIR), or recursive, filters are less attractive mainly because of the stability and the difficulties associated with their adaptive algorithms. Hyperstability theory when simplified and adapted for digital signal processing offers a new class of IIR filters, simple hyperstable adaptive recursive filters (SHARFs), which is directly related to strictly positive real (SPR) transfer functions. One of the most important drawbacks of the SHARF algorithm is the presence of the unknown denominator in the transfer function that must be SPR in order to ensure convergence. In this paper, SHARF is investigated with SPR transfer functions designed without any priori knowledge of the filter parameters by the pole–zero placement on the unit circle method and made self-adjusting. To demonstrate self-adjustment of the algorithm, SHARF algorithm using constraint least-mean square (LMS) method is applied to a pure four-pole autoregressive process.

Solving frictional contact problems by two aggregate-function-based algorithms

Three dimensional frictional contact problems are formulated as linear complementarity problems based on the parametric variational principle. Two aggregate-function-based algorithms for solving complementarity problems are proposed. One is called the self-adjusting interior point algorithm, the other is called the aggregate function smoothing algorithm. Numerical experiment shows the efficiency of the proposed two algorithms.

A Self-Adjusting Algorithm for Driver Scheduling

Public transport driver scheduling is a world wide problem, which is NP-hard. Although some mathematically based methods are being used in the transport industry, there is still much scope for improvements. This paper presents a novel evolutionary approach that simulates the self-adjusting process on a single schedule. Five factors characterized by fuzzy membership functions are first aggregated to evaluate the shift structure. This evaluating function is incorporated into a constructing heuristic to make shift selection. A self-adjusting algorithm is then designed to guide the constructing heuristic to improve a given initial schedule iteratively. In each generation an unfit portion of the working schedule is removed. Broken schedules are repaired by the constructing heuristic until stopping condition is met. Experimental results on real-world driver scheduling problems has demonstrated the success of the proposed approach.

Linescan camera evaluation of SSM/I 85.5 GHz sea ice retrieval

Retrievals of total sea ice concentration from four algorithms using the 85.5 GHz vertically and horizontally polarized channels of the Special Sensor Microwave Imager (SSM/I) over the marginal ice zone in the Barents and Greenland Seas are compared with retrievals of total sea ice concentration from helicopter-borne linescan camera observations made during a cruise of the R/V Polarstern during May–June 1997. The goals are to evaluate (1) SSM/I 85.5 GHz retrievals of total sea ice concentration for climatological purposes, and (2) the ability of 85.5 GHz data to show the sea ice edge through cloud cover, for operational purposes. The SSM/I 85.5 GHz channels offer a spatial resolution of 12.5 km, which is sufficient to resolve ice edge features and small polynyas; however, there is generally more atmospheric contamination of the sea ice signal at 85.5 GHz than at the lower frequencies (19 and 37 GHz) traditionally used for sea ice remote sensing. A self-adjusting algorithm that performs a nonlinear correction for atmospheric moisture, without explicit atmospheric input data, yields the best accuracy over total sea ice concentrations greater than 30%. However, this algorithm can misclassify clouds over open water as sea ice, and is therefore unreliable for locating the sea ice edge. The best algorithm for locating the sea ice edge is found to be the SEA LION algorithm, which explicitly uses meteorological reanalysis data to correct for atmospheric contamination. For total sea ice concentrations in the range 20–70%, empirical 85.5 GHz hybrids of lower-frequency algorithms developed at the NASA Goddard Space Flight Center can improve the accuracy of these algorithms.

Adaptive compensation for position error signal nonlinearity

The magnetoresistive (MR) magnetic head is a poor positioning transducer for a disk file's servo control system because its positioning response is nonlinear with radial displacement. This paper shows how the MR head's poor positioning properties are alleviated by a self-adjusting adaptive algorithm that allows a disk file to linearize its own servo position error signal (PES). The adaptive linearizer uses a nonlinear state estimator whose nonlinearity adjusts to match the nonlinearity of the PES. As the match between the two nonlinearities adaptively improves, the state estimator gives increasingly accurate estimates of the true actuator position.

An Algorithm for Adaptive Correction of Radar Images

Problems are considered of construction of adaptive algorithms for synthesizing of radar images of underlying surface from signals of coherent airborne radar. Algorithm is proposed pertaining to class of self-adjusting algorithms in which information on trajectory errors is extracted directly from reflected signal. Slant-distance averaged estimate of shift of the Doppler-spectrum on synthesizing interval and higher derivatives of the estimate are used as information parameters. This allows to automatically correct images caused by carrier flight-velocity errors and to take into account errors caused by disregarded acceleration and by rate of change of the acceleration. Results are presented of signals processing of coherent synthetic-aperture radar (SAR). Characteristic properties are considered of operation of proposed algorithm under various conditions.

Analyzing self-adjusting linear list algorithms with deletions and unsuccessful searches

In (Hui and Martel, 1993), we designed and analyzed efficient self-adjusting linear list algorithms. Our analysis proves that a self-adjusting linear list algorithm, MP, is competitive to a large class of offline adversaries, where the operations are successful searches, unsuccessful searches, and insertions. Analysis of deletions is listed as an open question. This paper presents an improved version of MP which is also able to handle deletions efficiently, and proves that the new MP algorithm is 6-competitive to offline adversaries when considering successful searches, unsuccessful searches, insertions, and deletions.

Unsuccessful Search in Self-Adjusting Data Structures

This paper introduces a general technique for speeding up unsuccessful search using very little extra space (two bits per key). This technique is applicable to many data structures including linear lists and search trees. For linear lists we obtain on-line algorithms for processing a sequence of successful and unsuccessful searches which are competitive with strong off-line algorithms. In a virtual memory environment our self-adjusting algorithm for multiway search trees is competitive with an optimal static multiway tree and will often outperform the static tree. We are also able to extend several other results for successful search to include both successful and unsuccessful searches.

A self-adjusting algorithm for byzantine agreement

Byzantine Agreement is important both in the theory and practice of distributed computing. However, protocols to reach Byzantine Agreement are usually expensive both in the time required as well as in the number of messages exchanged. In this paper, we present a self-adjusting approach to the problem. The Mostly Byzantine Agreement is proposed as a more restrictive agreement problem that requires that in the consecutive attempts to reach agreement, the number of disagreements (i.e., failures to reach Byzantine Agreement) is finite. For t faulty processes, we give an algorithm that has at most t disagreements for 4t or more processes. Another algorithm is given for n ≥ 3t + 1 processes with the number of disagreements below t2/2. Both algorithms use O(n3) message bits for binary value agreement.

An adaptive-mesh finite-difference solution method for the Navier-Stokes equations

A variable-spacing grid system for finite-difference calculations is presented. The system allows single points to be added to or deleted from the mesh independently of each other, while maintaining each point at the center of a symmetrical cross formed with four other mesh points. The single finite-difference form of the steady, incompressible Navier-Stokes equations necessary for use with this system is written in a suitable form insuring stability, and the addition-deletion procedure is easily automated and transformed into a self-adjusting algorithm capable of recognizing the high-gradient regions of the solution field and selectively refining the mesh in those regions. It also determines the best-suited number of points for the calculation. The method is finally applied to two test cases to show its performance.