Redundant Solutions Research Articles

Food and Green Space in Cities: A Resilience Lens on Gardens and Urban Environmental Movements

This article examines the role played by urban gardens during historical collapses in urban food supply lines and identifies the social processes required to protect two critical elements of urban food production during times of crisis—open green spaces and the collective memory of how to grow food. Advanced communication and transport technologies allow food sequestration from the farthest reaches of the planet, but have markedly increasing urban dependence on global food systems over the past 50 years. Simultaneously, such advances have eroded collective memory of food production, while suitable spaces for urban gardening have been lost. These factors combine to heighten the potential for food shortages when—as occurred in the 20th century—major economic, political or environmental crises sever supply lines to urban areas. This paper considers how to govern urban areas sustainably in order to ensure food security in times of crisis by: evincing the effectiveness of urban gardening during crises; showing how allotment gardens serve as conduits for transmitting collective social-ecological memories of food production; and, discussing roles and strategies of urban environmental movements for protecting urban green space. Urban gardening and urban social movements can build local ecological and social response capacity against major collapses in urban food supplies. Hence, they should be incorporated as central elements of sustainable urban development. Urban governance for resilience should be historically informed about major food crises and allow for redundant food production solutions as a response to uncertain futures.

Tackling with redundant design solutions through axiomatic design

Redundancy is a multifaceted issue that might be addressed according to the context, and Axiomatic Design (AD) is an appropriate approach to deal with it in engineering applications. According to the AD’s Independence Axiom, design solutions can be uncoupled, decoupled or coupled. Uncoupled and decoupled solutions are seen as “good design”, the uncoupled ones being the best, while coupled designs are regarded as “poor design”. Redundant designs belong to a special kind of designs, in which the number of design parameters is larger than the number of functional requirements. Such designs can also be uncoupled, decoupled or coupled and this paper provides a systematic way for identifying the good and the poor redundant design solutions, as well as some clues to help generating good redundant designs. The paper also delivers simple examples to illustrate diverse issues of redundant designs, and presents seven new theorems two new corollaries that help identifying and generating good redundant designs.

로봇의 어포던스 지각 과정 및 계층 분석법을 이용한 우선 순위 동작 결정

This paper presents a new behavior planning scheme for autonomous robots, allowing them to handle various objects used in our daily lives. The key idea underlying the proposed scheme is to use affordance concepts that provide a robot with action possibilities triggered by a relation between the robot and objects around it. Specifically, the robot attempts to find the affordances and to determine the most adequate action among them. Through a series of the perception processes, robot motions can be planned and performed to complete assigned tasks. What is of particular importance from the practical point of view is a decision making capability to determine the best choice by comparing the human`s body characteristics and behavioral patterns as criteria with action possibilities as alternatives. For this, the analytic hierarchy process (AHP) technique is employed to systematically evaluate the correlation between the criteria and the alternatives. Moreover, the alternatives arranged in order of priority through the decision making process enable the robot to have redundant solutions for the assigned task, resulting in flexible motion generation. The effectiveness and validity of the proposed scheme are verified by performing extensive simulations using objects of our daily use.

Archaeal virus with exceptional virion architecture and the largest single-stranded DNA genome

Known viruses build their particles using a restricted number of redundant structural solutions. Here, we describe the Aeropyrum coil-shaped virus (ACV), of the hyperthermophilic archaeon Aeropyrum pernix, with a virion architecture not previously observed in the viral world. The nonenveloped, hollow, cylindrical virion is formed from a coiling fiber, which consists of two intertwining halves of a single circular nucleoprotein. The virus ACV is also exceptional for its genomic properties. It is the only virus with a single-stranded (ss) DNA genome among the known hyperthermophilic archaeal viruses. Moreover, the size of its circular genome, 24,893 nt, is double that of the largest known ssDNA genome, suggesting an efficient solution for keeping ssDNA intact at 90-95 °C, the optimal temperature range of A. pernix growth. The genome content of ACV is in line with its unique morphology and confirms that ACV is not closely related to any known virus.

Multiple parents crossover operators: A new approach removes the overlapping solutions for sequencing problems

Maintaining population diversity throughout generations of Genetic Algorithms (GAs) is key to avoid premature convergence. Redundant solutions is one cause for the decreasing population diversity. To prevent the negative effect of redundant solutions, we propose a framework that is based on the multi-parents crossover (MPX) operator embedded in GAs. Because MPX generates diversified chromosomes with good solution quality, when a pair of redundant solutions is found, we would generate a new offspring by using the MPX to replace the redundant chromosome. Three schemes of MPX will be examined and will be compared against some algorithms in literature when we solve the permutation flowshop scheduling problems, which is a strong NP-Hard sequencing problem. The results indicate that our approach significantly improves the solution quality. This study is useful for researchers who are trying to avoid premature convergence of evolutionary algorithms by solving the sequencing problems.

Two-point paraxial traveltimes in an inhomogeneous anisotropic medium

SUMMARY We derive formulae for the approximate computation of two-point paraxial traveltimes (traveltimes between two points) for points arbitrarily chosen in a paraxial vicinity of a reference ray computed in a smoothly varying inhomogeneous anisotropic medium containing structural interfaces. The formulae have a form of the Taylor expansion in Cartesian coordinates of the two-point paraxial traveltime or its square to the quadratic terms. The coefficients of the expansion depend on quantities obtained by ray tracing in Cartesian coordinates and by dynamic ray tracing in ray-centred coordinates. Alternatively, the dynamic ray tracing can be performed in Cartesian coordinates. The advantages of the approach based on dynamic ray tracing in ray-centred coordinates are its efficiency and elimination of possible complications that may arise from the redundant fundamental solutions of dynamic ray tracing in Cartesian coordinates (the ray-tangent and non-eikonal solutions). As a by-product, we also obtain simple formulae for the slowness vectors at the two points in the paraxial vicinity of the reference ray. They belong to a paraxial ray passing through these points. Potential applications of the proposed formulae consist in the fast and flexible two-point traveltime calculations from sources to receivers specified in Cartesian coordinates and situated close to a reference ray, along which dynamic ray tracing has been performed. The formulae for the paraxial slowness vectors can be used in two-point ray tracing.

Assisted Research and Optimization of the Proper Neural Network Solving the Inverse Kinematics Problem

Finding the better solution of the neural network design to solve the inverse kinematics problem with the minimum of the trajectory errors is very difficult, because there are many variable parameters and many redundant solutions. The presented paper show the assisted research of the influences of some more important parameters to the final end-effector trajectory errors of the proposed neural network model solving the inverse kinematics problem. We were been studied the number of neurons in each layers, the sensitive function for the first and second layer, the magnifier coefficient of the trajectory error, the variable step of the time delay and the position of this block, the different cases of target data and the case when the hidden target data were adjusted. All obtained results were been verified by applying the proper direct kinematics virtual LabVIEW instrumentation. Finally we were obtained one optimal Sigmoid Bipolar Hyperbolic Tangent Neural Network with Time Delay and Recurrent Links (SBHTNN(TDRL)) type, what can be used to solve the inverse kinematics problem with maximum 4% of trajectory errors.

Area efficient concurrent error detection and correction for parallel filters

In modern signal processing circuits, it is common to find several filters operating in parallel. Proposed is an area efficient technique to detect and correct single errors occurring in pairs of parallel filters that have either the same input data or the same impulse response. The technique uses a primary implementation comprised of two independent filters and a redundant implementation that shares input data between both filters so as to detect and correct errors.The area cost of the proposed scheme is shown to be slightly more than double that of the unprotected filter, whereas the conventional triple modular redundancy solution requires an area three times that of the unprotected filter.

A novel hybrid genetic algorithm for the open shop scheduling problem

In this paper, a hybrid genetic algorithm is proposed for the open shop scheduling problem with the objective of minimizing the makespan. In the proposed algorithm, a specialized crossover operator is used that preserves the relative order of jobs on machines and a strategy is applied to prevent from searching redundant solutions in the mutation operator. Moreover, an iterative optimization heuristic is employed which uses the concept of randomized active schedules, a dispatching index based on the longest remaining processing time rule and a lower bound to further decrease the search space. Computational results show that the proposed algorithm outperforms other genetic algorithms and is very competitive with well-known metaheuristics available in the literature.

Automated synthesis of multiple analog circuits using evolutionary computation for redundancy-based fault-tolerance

Analog circuits are one of the most important parts of modern electronic systems and the failure of electronic hardware presents a critical threat to the completion of modern aircraft, spacecraft, and robot missions. Compared to digital circuits, designing fault-tolerant analog circuits is a difficult and knowledge-intensive task. A simple but powerful method for robustness is a redundancy approach to use multiple circuits instead of single one. For example, if component failures occur, other redundant components can replace the functions of broken parts and the system can still work. However, there are several research issues to make the redundant system automatically. In this paper, we used evolutionary computation to generate multiple analog circuits automatically and then we combined the solutions to generate robust outputs. Evolutionary computation is a natural way to produce multiple redundant solutions because it is a population-based search. Experimental results on the evolution of the low-pass, high-pass and band-stop filters show that the combination of multiple evolved analog circuits produces results that are more robust than those of the best single circuit.

Background Calibration Techniques for Low-Power and High-Speed Data Conversion

Progress of roles and schemes of calibration techniques in data converters are reviewed. Correction techniques of matching error and nonlinearity in analog circuits have been developed by digital assist using high-density and low-power digital circuits. The roles of the calibration are not only to improve accuracy but also to reduce power dissipation and chip area. Among various calibration schemes, the background calibration has significant advantages to achieve robustness to fast ambient change. Firstly the nonlinearity calibrations for pipeline ADCs are reviewed. They have required new solutions for redundancy of the circuits, an error estimation algorithm and reference signals. Currently utilizing the calibration techniques, the performance of 100Msps and 12bit has been achieved with 10mW power dissipation. Secondly the background calibrations of matching error in flash ADC and DAC with error feedback to the analog circuits are described. The flash ADC utilizes the comparator offset correction with successive approximation algorithm. The DAC adopts a self current matching scheme with an analog memory. Measured dissipation power of the ADC is 0.38mW at 300MHz clock. Effects of the background calibration to suppress crosstalk noise are also discussed.

Redundant exact solutions of nonlinear differential equations

We analyze the paper by Wazwaz and Mehanna [Wazwaz AM, Mehanna MS. A variety of exact travelling wave solutions for the (2 + 1)-dimensional Boiti–Leon–Pempinelli equation. Appl Math Comput 2010;217:1484–90]. The authors claim that they have found exact solutions of the (2 + 1)-dimensional Boiti–Leon–Pempinelli equation using the tanh–coth method and the Exp-function method. We demonstrate that two of their solutions are incorrect. All the others can be simplified and they are the partial cases of the well-known solution. Wazwaz and Mehanna made a number of typical mistakes in finding exact solutions of nonlinear differential equations. Taking the results of this paper we introduce the definition of redundant exact solutions for the nonlinear ordinary differential equations.

Motor Learning through Induced Variability at the Task Goal and Execution Redundancy Levels

ABSTRACT The authors examined the influence of introducing variability at two different levels in the learning of a striking task. Variability at the task goal level was introduced by changing target location, whereas variability at the execution redundancy level was introduced by using an intermediate target that constrained participants to use different paths from trial to trial to strike the same target. After practice, participants were transferred to 2 test conditions: (a) a fixed-target test, where the position of the target was unchanged; and (b) a variable-target test, where the position of the target was varied from trial to trial. The results from the manipulation at the task goal level were consistent with predictions from the specificity of practice hypothesis. In both the fixed- and variable-target tests, the best performance was achieved by the group that had practiced in the condition matching the test condition. At the execution-redundancy level, practicing multiple solutions to achieve the task goal did not improve performance in either the fixed- or variable-target tests. These results show that introducing variability at the task goal and execution redundancy levels has different effects on learning and generalization and that practice schedules that constrain the participant to use redundant solutions may not facilitate learning.

Differential RAID

SSDs exhibit very different failure characteristics compared to hard drives. In particular, the bit error rate (BER) of an SSD climbs as it receives more writes. As a result, RAID arrays composed from SSDs are subject to correlated failures. By balancing writes evenly across the array, RAID schemes can wear out devices at similar times. When a device in the array fails towards the end of its lifetime, the high BER of the remaining devices can result in data loss. We propose Diff-RAID, a parity-based redundancy solution that creates an age differential in an array of SSDs. Diff-RAID distributes parity blocks unevenly across the array, leveraging their higher update rate to age devices at different rates. To maintain this age differential when old devices are replaced by new ones, Diff-RAID reshuffles the parity distribution on each drive replacement. We evaluate Diff-RAID's reliability by using real BER data from 12 flash chips on a simulator and show that it is more reliable than RAID-5, in some cases by multiple orders of magnitude. We also evaluate Diff-RAID's performance using a software implementation on a 5-device array of 80 GB Intel X25-M SSDs and show that it offers a trade-off between throughput and reliability.

Mixed-Integer Linear Programming Monolithic Formulations for Lot-Sizing and Scheduling of Single-Stage Batch Facilities

This paper presents a pair of mixed-integer linear programming (MILP) continuous-time formulations for the simultaneous lot-sizing and scheduling of single-stage multiproduct batch facilities. Both approaches can handle multiple customer orders per product at different due dates as well as variable processing times. To match product demands, several batches can be allocated to a single requirement and, at the same time, a single batch may be used to satisfy multiple orders. Through a novel procedure, a predefined set of batches for each order with enough elements to guarantee optimality is generated. The two proposed formulations deal with batch sequencing decisions in a different manner. One of them rigorously arranges individual batches assigned to the same unit, while the other sequences clusters of batches sharing the same product and due date, and processed in the same equipment item. Grouping batches into clusters seeks to reduce the number of product changeovers. The final contents of clusters are model decisions. Powerful symmetry breaking constraints based on allocation variables to avoid redundant solutions were also developed. Three cases studies involving up to 56 batches have been solved. The two formulations provide very good results at quite competitive CPU times when compared with prior monolithic techniques. Moreover, the approximate cluster-based method was able to solve very large problems in an efficient manner. It was validated by comparing its results with the ones provided by the rigorous model.

Differential RAID

Deployment of SSDs in enterprise settings is limited by the low erase cycles available on commodity devices. Redundancy solutions such as RAID can potentially be used to protect against the high Bit Error Rate (BER) of aging SSDs. Unfortunately, such solutions wear out redundant devices at similar rates, inducing correlated failures as arrays age in unison. We present Diff-RAID, a new RAID variant that distributes parity unevenly across SSDs to create age disparities within arrays. By doing so, Diff-RAID balances the high BER of old SSDs against the low BER of young SSDs. Diff-RAID provides much greater reliability for SSDs compared to RAID-4 and RAID-5 for the same space overhead, and offers a trade-off curve between throughput and reliability.

Emergent flexibility in motor learning

We examined the effect of exploring redundant solutions during practice in enhancing the ability to flexibly use them to achieve a task goal. Three groups used different degrees of path redundancy to perform a virtual interception task in which they attempted to hit a stationary target by moving around a stationary obstacle. The low-variability group always practiced with the same position of the obstacle on all trials. The medium-variability and high-variability groups practiced with the obstacle in different positions within a range of 1 and 2 cm respectively. After eight blocks of practice, all participants were transferred to two tests: (a) a fixed obstacle test where the condition was the same as that practiced by the low-variability group, and (b) a variable obstacle test where the condition was the same as that practiced by the high-variability group. Results showed that the low-variability group had the most accurate performance both in the fixed obstacle and the variable obstacle test. The low-variability group showed the least path variability during the fixed obstacle test but was also able to adapt to the different positions of the obstacle during the variable obstacle test. It appears that flexibility in interceptive tasks is emergent from learning a particular task-relevant parameter related to the target location.

Diversity of efficient coding solutions for a population of noisy linear neurons

Methods E cient coding posits that neural resources (cost) should e ciently be utilized for signal representation (objective). (5, 14) • Dependency on the objective function has been well examined. (13, 6, 8) • Dependency on the cost function: For a single neuron: output range, ring rate, or variance constraints. (11, 4, 14) For multiple neurons: less studied. We examine the e ects of costs that are likely to be relevant in neural populations.

Influence of motor learning on utilizing path redundancy

We examined the role of motor learning in influencing the utilization of path redundancy in an interception task. Participants used a pen on a digitizing tablet with the goal of moving to intercept a stationary target shown on a computer screen. Concurrent visual feedback of the cursor and knowledge of results were provided. The changes in spatial variability after extended practice and the correlations between spatial locations on the path were consistent with the view that path redundancy was utilized. However, paths also became less variable with practice, indicating that less of the solution space was utilized. There was also a sequential relation in the movement paths from trial to trial with a tendency to use more similar movement paths on successive trials. These results show that models that account for both redundancy and invariance may be better able to capture the changes in variability with learning. Further, the sequential dependence in the paths suggests that the exploration of redundant solutions is a systematic search process.

Performance of RTK Positioning in Forest Conditions: Case Study

This paper presents the performance of the real-time kinematic (RTK) technique in very severe conditions. The measurements were conducted in forest conditions during two different experiments. Two Global Positioning System receivers were used: an Ashtech Z-Surveyor and a Z-Xtreme. Radiomodems were used for transmitting RTK corrections from a reference station to a rover. The RTK measurements were performed during quite good satellite configuration. The use of RTK technology, based on the Ashtech Z-Xtreme receiver as a rover in forest terrain, allowed centimeter accuracy to be obtained. However, practical experiments showed that there is a need for redundant independent RTK solutions based on repeated independent ambiguity reinitializations. Although gross errors might occur, the RTK technique can be very helpful technology for centimeter positioning in woodland areas.