Hexadecimal Aggregate Approximation Representation and Classification of Time Series Data

Time series data are widely found in finance, health, environmental, social, mobile and other fields. A large amount of time series data has been produced due to the general use of smartphones, various sensors, RFID and other internet devices. How a time series is represented is key to the efficient and effective storage and management of time series data, as well as being very important to time series classification. Two new time series representation methods, Hexadecimal Aggregate approXimation (HAX) and Point Aggregate approXimation (PAX), are proposed in this paper. The two methods represent each segment of a time series as a transformable interval object (TIO). Then, each TIO is mapped to a spatial point located on a two-dimensional plane. Finally, the HAX maps each point to a hexadecimal digit so that a time series is converted into a hex string. The experimental results show that HAX has higher classification accuracy than Symbolic Aggregate approXimation (SAX) but a lower one than some SAX variants (SAX-TD, SAX-BD). The HAX has the same space cost as SAX but is lower than these variants. The PAX has higher classification accuracy than HAX and is extremely close to the Euclidean distance (ED) measurement; however, the space cost of PAX is generally much lower than the space cost of ED. HAX and PAX are general representation methods that can also support geoscience time series clustering, indexing and query except for classification.

Approximating median absolute deviation with bounded error

The median absolute deviation (MAD) is a statistic measuring the variability of a set of quantitative elements. It is known to be more robust to outliers than the standard deviation (SD), and thereby widely used in outlier detection. Computing the exact MAD however is costly, e.g., by calling an algorithm of finding median twice, with space cost O ( n ) over n elements in a set. In this paper, we propose the first fully mergeable approximate MAD algorithm, OP-MAD, with one-pass scan of the data. Remarkably, by calling the proposed algorithm at most twice, namely TP-MAD, it guarantees to return an (ϵ, 1)-accurate MAD, i.e., the error relative to the exact MAD is bounded by the desired ϵ or 1. The space complexity is reduced to O ( m ) while the time complexity is O ( n + m log m ), where m is the size of the sketch used to compress data, related to the desired error bound ϵ. To get a more accurate MAD, i.e., with smaller ϵ, the sketch size m will be larger, a trade-off between effectiveness and efficiency. In practice, we often have the sketch size m ≪ n , leading to constant space cost O (1) and linear time cost O ( n ). The extensive experiments over various datasets demonstrate the superiority of our solution, e.g., 160000× less memory and 18x faster than the aforesaid exact method in datasets pareto and norm . Finally, we further implement and evaluate the parallelizable TP-MAD in Apache Spark, and the fully mergeable OP-MAD in Structured Streaming.

Textile-based cyanobacteria biocomposites for potential environmental remediation applications

Microalgae and cyanobacteria are effective platforms for environmental remediation (phycoremediation), particularly of air and water. There is limited scope to deploy suspension cultures due to space, cost and maintenance challenges—driving an imperative towards biofilm-based treatment systems; however, these systems are ill-equipped for rapid and mobile deployment. In this study we explored the main technical challenges to developing cheap, accessible and low-maintenance engineered biofilm systems (biocomposites) comprising cyanobacteria (Synechococcus elongatus) immobilised to a range of textiles (n = 4) by natural or synthetic latex binders (n = 16), chitosan or shellac. Biocomposite viability (measured as net CO2 uptake) was assessed over 20 days in semi-batch trials. No maintenance was required during this period as the humidity within the reactor was sufficient to support metabolism. Two commercial natural latex binders (AURO 320 and 321) supported strong growth within the biocomposite, outperforming suspension controls. There was variation in textiles performance, with an 80/20 polyester-cotton blend performing most consistently. Biocomposite formulation was varied in terms of binder solids content and cell loading rate, with 5% solids and 2.5% cell loading the most effective combination. We demonstrate the technical feasibility of fabricating functional textile-based cyanobacteria biocomposites and discuss this within the context of developing decentralised wastewater treatment services.

Space-Efficient Key-Policy Attribute-Based Encryption from Lattices and Two-Dimensional Attributes

Linear secret-sharing scheme (LSSS) is a useful tool for supporting flexible access policy in building attribute-based encryption (ABE) schemes. But in lattice-based ABE constructions, there is a subtle security problem in the sense that careless usage of LSSS-based secret sharing over vectors would lead to the leakage of the master secret key. In this paper, we propose a new method that employs LSSS to build lattice-based key-policy attribute-based encryption (KP-ABE) that resolves this security issue. More specifically, no adversary can reconstruct the master secret key since we introduce a new trapdoor generation algorithm to generate a strong trapdoor (instead of a lattice basis), that is, the master secret key, and remove the dependency of the master secret key on the total number of system attributes. Meanwhile, with the purpose of reducing the storage cost and support dynamic updating on attributes, we extended the traditional 1-dimensional attribute structure to 2-dimensional one. This makes our construction remarkably efficient in space cost, with acceptable time cost. Finally, our scheme is proved to be secure in the standard model.

Wide-Speed Range Sensorless Control of an IPM Motor for Multi-Purpose Applications

In a growing number of battery-driven applications the need of removing any position and speed transducer is taking over due to space, cost and mechanical reliability constraints, further than making the installation easier as requiring less wiring. This paper presents the development of a sensorless algorithm capable of running an Interior Permanent Magnet Synchronous Machine (IPMSM), assuring constant torque production in the whole speed range, form standstill to high speeds. This is achieved with an hybrid method: at standstill and very low speeds the saliency of the IPM is exploited through an High Frequency Signal Injection (HFSI), which assures a robust estimation of the rotor position. At medium to high speeds an advanced V-I estimator is adopted in order to enhance the motor performances. The developed algorithm comes out of being highly scalable as it requires very little tuning, resulting in a multi-purpose application which can be employed with any motor size.

FreshJoin: An Efficient and Adaptive Algorithm for Set Containment Join

This paper revisits set containment join (SCJ) problem, which uses the subset relationship (i.e., $$\subseteq$$⊆) as condition to join set-valued attributes of two relations and has many fundamental applications in commercial and scientific fields. Existing in-memory algorithms for SCJ are either signature-based or prefix-tree-based. The former incurs high CPU cost because of the enumeration of signatures, while the latter incurs high space cost because of the storage of prefix trees. This paper proposes a new adaptive parameter-free in-memory algorithm, named as frequency-hashjoin or $${\mathsf {FreshJoin}}$$FreshJoin in short, to evaluate SCJ efficiently. $${\mathsf {FreshJoin}}$$FreshJoin builds a flat index on-the-fly to record three kinds of signatures (i.e., two least frequent elements and a hash signature whose length is determined adaptively by the frequencies of elements in the universe set). The index consists of two sparse inverted indices and two arrays which record hash signatures of all sets in each relation. The index is well organized such that $${\mathsf {FreshJoin}}$$FreshJoin can avoid enumerating hash signatures. The rationality of this design is explained. And, the time and space cost of the proposed algorithm, which provide a rule to choose $${\mathsf {FreshJoin}}$$FreshJoin from existing algorithms, are analyzed. Experiments on 16 real-life datasets show that $${\mathsf {FreshJoin}}$$FreshJoin usually reduces more than 50% of space cost while remains as competitive as the state-of-the-art algorithms in running time.

An integrated design approach for class-based block stacked warehouse

Purpose This paper aims to develop an approach to design a warehouse that uses class-based storage policy in a way that minimizes both space cost and material handling cost. Design/methodology/approach The authors argue for and develop an optimization model for joint determination of lane depth, lateral width and product partitions for minimizing the sum of handling and space costs. In doing so, the assumption of perfect sharing is also relaxed. Using computational experiments, the authors characterize the operating conditions based on pick density and cost ratio. The authors further outline an approach to decide the conditions under which it is advantageous to implement multiple classes. Findings More classes are preferred when both the pick density and cost ratio are higher and vice versa. Factors such as demand skewness, lane depth and stacking height affect the space-sharing dynamics. Practical implications The paper gives the practical insights on when the conditions under which it is advisable to partition a warehouse into a certain number of classes instead of maintaining and when to maintain as a single-class block. It also gives a method to estimate the space-sharing factor, given a combination of operating parameters. Originality/value Very few studies have seen class-based storage policy in the context of block stacked warehouse layout. Further, block stacking designs have mostly been approached with the objective of minimizing just the space cost. This study contributes to the literature by developing an integrated model, which has the practical utility.

A Game of Surface Codes: Large-Scale Quantum Computing with Lattice Surgery

Given a quantum gate circuit, how does one execute it in a fault-tolerant architecture with as little overhead as possible? In this paper, we discuss strategies for surface-code quantum computing on small, intermediate and large scales. They are strategies for space-time trade-offs, going from slow computations using few qubits to fast computations using many qubits. Our schemes are based on surface-code patches, which not only feature a low space cost compared to other surface-code schemes, but are also conceptually simple~--~simple enough that they can be described as a tile-based game with a small set of rules. Therefore, no knowledge of quantum error correction is necessary to understand the schemes in this paper, but only the concepts of qubits and measurements.

Assessment of a reconfigurable manufacturing system

Purpose The purpose of this paper is to develop the methodology which can facilitate the concept of reconfiguration in the manufacturing system. Design/methodology/approach Design methodology includes the calculation of similarity matrix, formation of part family, and selection of part family. ALC algorithm has been used for part family formation and three criteria have been considered for the selection of part family. These criteria are reconfiguration effort, under-utilization cost, and floor space cost. AHP has been used to calculate the weights of criteria and reference ideal method has been used for the selection of alternatives. Findings In the manufacturing system, machines should be grouped on the basis of reconfiguration cost. When the time period is less, light machines and Group 1 machines are added and removed. In the case study, the concept of reconfiguration is useful for families (A, B, C, D). Machines can be reused by adding/removing some modules of machines. The concept of reconfiguration becomes more useful when it is implemented with lean manufacturing. Lean manufacturing techniques Jidoka and Poka-yoke are used to increase the diagnosability of the system. Practical implications Industrial case study has been considered. Social implications Market competition is increasing rapidly and it increases the demand and variety of products, due to which manufacturing enterprises are forced to adapt a manufacturing system which can adjust its capacity and functionality quickly at low cost. To reconfigure manufacturing system from one product/product family to another product/product family, changes can be done in hardware and/or software components in response to sudden changes in the market or in regulatory requirements. Originality/value An integrated approach for reconfiguration has been proposed considering the industrial application. It includes weighted Jaccard function, ALCA, AHP, RIM. The methodology for calculation of reconfiguration effort, under-utilization cost, and floor space cost has been presented for industrial case.