Memory-based Scheme Research Articles

Extension of King’s Iterative Scheme by Means of Memory for Nonlinear Equations

We developed a new family of optimal eighth-order derivative-free iterative methods for finding simple roots of nonlinear equations based on King’s scheme and Lagrange interpolation. By incorporating four self-accelerating parameters and a weight function in a single variable, we extend the proposed family to an efficient iterative scheme with memory. Without performing additional functional evaluations, the order of convergence is boosted from 8 to 15.51560, and the efficiency index is raised from 1.6817 to 1.9847. To compare the performance of the proposed and existing schemes, some real-world problems are selected, such as the eigenvalue problem, continuous stirred-tank reactor problem, and energy distribution for Planck’s radiation. The stability and regions of convergence of the proposed iterative schemes are investigated through graphical tools, such as 2D symmetric basins of attractions for the case of memory-based schemes and 3D stereographic projections in the case of schemes without memory. The stability analysis demonstrates that our newly developed schemes have wider symmetric regions of convergence than the existing schemes in their respective domains.

Genetic Algorithm with Random and Memory Immigrant Strategies for Solving Dynamic Load Balanced Clustering Problem in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), clustering is an effective method to distribute the load equally among all the nodes as compared to flat network architecture. Due to the dynamic nature of the network, the clustering process can be viewed as a dynamic optimization problem and the conventional computational intelligence techniques are not enough to solve these problems. The Dynamic Genetic Algorithm (DGA) addresses these problems with the help of searching optimal solutions in new environments. Therefore the dynamic load-balanced clustering process is modeled using the basic components of standard genetic algorithm and then the model is enhanced is using immigrants and memory-based schemes to elect suitable cluster heads. The metrics nodes’ residual energy level, node centrality, and mobility speed of the nodes are considered to elect the load-balanced cluster heads and the optimal number of cluster members are assigned to each cluster head using the proposed DGA schemes such as Random Immigrants Genetic Approach (RIGA), Memory Immigrants Genetic Approach (MIGA), and Memory and Random Immigrants Genetic Approach (MRIGA). The simulation results show that the proposed DGA scheme MRIGA outperforms well as compared with RIGA and MIGA in terms of various performance metrics such as the number of nodes alive, residual energy level, packet delivery ratio, end-to-end delay, and overhead for the formation of clusters.

Resource requirements for efficient quantum communication using all-photonic graph states generated from a few matter qubits

Quantum communication technologies show great promise for applications ranging from the secure transmission of secret messages to distributed quantum computing. Due to fiber losses, long-distance quantum communication requires the use of quantum repeaters, for which there exist quantum memory-based schemes and all-photonic schemes. While all-photonic approaches based on graph states generated from linear optics avoid coherence time issues associated with memories, they outperform repeater-less protocols only at the expense of a prohibitively large overhead in resources. Here, we consider using matter qubits to produce the photonic graph states and analyze in detail the trade-off between resources and performance, as characterized by the achievable secret key rate per matter qubit. We show that fast two-qubit entangling gates between matter qubits and high photon collection and detection efficiencies are the main ingredients needed for the all-photonic protocol to outperform both repeater-less and memory-based schemes.

Memory based scheme to monitor non-random small shift patterns in manufacturing process

Consistent high-quality and defect-free production is the demand of the day. The product recall not only increases engineering and manufacturing cost but also affects the quality and the reliability of the product in the eye of users. The monitoring and improvement of a manufacturing process are the strength of statistical process control. In this article we propose a process monitoring memory-based scheme for continuous data under the assumption of normality to detect small non-random shift patterns in any manufacturing or service process. The control limits for the proposed scheme are constructed. The in-control and out-of-control average run length (AVL) expressions have been derived for the performance evaluation of the proposed scheme. Robustness to non-normality has been tested after simulation study of the run length distribution of the proposed scheme, and the comparisons with Shewhart and exponentially weighted moving average (EWMA) schemes are presented for various gamma and t-distributions. The proposed scheme is effective and attractive as it has one design parameter which differentiates it from the traditional schemes. Finally, some suggestions and recommendations are made for the future work.

A vestibular prosthesis with highly-isolated parallel multichannel stimulation.

This paper presents an implantable vestibular stimulation system capable of providing high flexibility independent parallel stimulation to the semicircular canals in the inner ear for restoring three-dimensional sensation of head movements. To minimize channel interaction during parallel stimulation, the system is implemented with a power isolation method for crosstalk reduction. Experimental results demonstrate that, with this method, electrodes for different stimulation channels located in close proximity ( mm) can deliver current pulses simultaneously with minimum inter-channel crosstalk. The design features a memory-based scheme that manages stimulation to the three canals in parallel. A vestibular evoked potential (VEP) recording unit is included for closed-loop adaptive stimulation control. The main components of the prototype vestibular prosthesis are three ASICs, all implemented in a 0.6- μm high-voltage CMOS technology. The measured performance was verified using vestibular electrodes in vitro.

Robustness analysis of privacy-preserving model-based recommendation schemes

Privacy-preserving model-based recommendation methods are preferable over privacy-preserving memory-based schemes due to their online efficiency. Model-based prediction algorithms without privacy concerns have been investigated with respect to shilling attacks. Similarly, various privacy-preserving model-based recommendation techniques have been proposed to handle privacy issues. However, privacy-preserving model-based collaborative filtering schemes might be subjected to shilling or profile injection attacks. Therefore, their robustness against such attacks should be scrutinized.In this paper, we investigate robustness of four well-known privacy-preserving model-based recommendation methods against six shilling attacks. We first apply masked data-based profile injection attacks to privacy-preserving k-means-, discrete wavelet transform-, singular value decomposition-, and item-based prediction algorithms. We then perform comprehensive experiments using real data to evaluate their robustness against profile injection attacks. Next, we compare non-private model-based methods with their privacy-preserving correspondences in terms of robustness. Moreover, well-known privacy-preserving memory- and model-based prediction methods are compared with respect to robustness against shilling attacks. Our empirical analysis show that couple of model-based schemes with privacy are very robust.

Statistical Characteristics of Biomimetic Image-Based Inverted Pendulum Control Systems Using Just-In-Time Method

In attempting to statistically answer the question, “In what ways should robots attempt or imitate human-like behavior?” We focus on a human stick-balancing task and a biomimetic visual servoing system for the stabilization of an inverted pendulum. In particular, we applied proportional and differential (PD) and Just-In-Time (JIT) control, which is a memory-based scheme. This method can stabilize the inverted pendulum for a certain period of time, similar to the case of humans.We analyzed the behavior of such mechanical systems statistically to determine the on-off intermittency and the power law in the human stick-balancing task. Statistically analyzing pendulum behavior may enable us to interpret human motor control.

2A1-N08 視覚-ロボットアームからなるJust-In-Time制御系の振舞いの統計解析(脳科学・神経科学とロボティクス)

Just-In-Time control is memory-based scheme which corresponds to the mechanism of human learning and memory. This method can stabilize the inverted pendulum for a certain amount of time just like humans. An analysis of the behavior of the pendulum may give an interpretation for the motor control mechanism of human.

人の視覚運動制御系を模擬したカメラ情報に基づく倒立振子むだ時間系のＪｕｓｔ‐Ｉｎ‐Ｔｉｍｅ制御

Humans can keep a stick on the fingertip upright position for a certain time after a brief exercise, although we have a large time delay due to the signal transfer in the optic nerve and due to information processing in the brain. Correspondingly in an experiment on the stabilization of the mechanical inverted pendulum, we use a normal frame rate camera as an angle sensor of the pendulum, and its large time delay makes the stabilization difficult. How can humans compensate the large time delay? In order to answer this question, we apply Just-In-Time method to the inverted pendulum control system. This memory-based scheme corresponds to the mechanism of human learning and memory. This method can stabilize the inverted pendulum for a certain amount of time just like humans. An analysis of the behavior of the pendulum may give an interpretation for the motor control mechanism of human.

Scale and Translation Invariant Collaborative Filtering Systems

Collaborative filtering systems are prediction algorithms over sparse data sets of user preferences. We modify a wide range of state-of-the-art collaborative filtering systems to make them scale and translation invariant and generally improve their accuracy without increasing their computational cost. Using the EachMovie and the Jester data sets, we show that learning-free constant time scale and translation invariant schemes outperforms other learning-free constant time schemes by at least 3% and perform as well as expensive memory-based schemes (within 4%). Over the Jester data set, we show that a scale and translation invariant Eigentaste algorithm outperforms Eigentaste 2.0 by 20%. These results suggest that scale and translation invariance is a desirable property.

A Tabular Approach Memory-Based Learning

One type of machine learning, called memory-based learning, which is characterized by memorization of observed events, has been investigated. Given an input vector, some data are retrieved from a memory and fed to an output computational function. Existing memory-based schemes were usually introduced, discussed, and treated as different, unrelated techniques. In this paper, we unify them into one category. A Memory-Based Learning Structure (MBLS) receives training samples and stores data at memory locations associated with selected (fired) vertices, which are assigned points in the input domain. Memory-based learning that uses a structured vertex distribution is categorized as a tabular approach. One merit of a tabular MBLS is that the size of allocated memory can be pre-determined and much smaller than the number of available observations. Another advantage is that the training process filters out noise automatically and makes the MBLS less sensitive. Since data are stored in memory directly addressable by queries, data retrieving is fast and memorization of vertex locations is unnecessary.

Processor- and memory-based checkpoint and rollback recovery

Several hardware-based techniques that support checkpoint and rollback recovery are presented. The focus is on hardware schemes for uniprocessors, shared-memory multiprocessors, and distributed virtual-memory systems. A taxonomy for processor and memory techniques based on the memory hierarchy is presented. This provides a basis for understanding subtle differences among the various schemes. Processor-based schemes that handle transient faults by using processor-based transparent rollback techniques and memory-based schemes that roll back data instead of instructions and can be integrated with the processor techniques or can be exploited by higher levels of software are discussed. >