Wyniki wyszukiwania - BazTech

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Sequential Classification of Palm Gestures Based on A* Algorithm and MLP Neural Network for Quadrocopter Control

Wodziński M., Krzyżanowska A.

Metrology and Measurement Systems

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2017

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Vol. 24, nr 2

265--276

EN

This paper presents an alternative approach to the sequential data classification, based on traditional machine learning algorithms (neural networks, principal component analysis, multivariate Gaussian anomaly detector) and finding the shortest path in a directed acyclic graph, using A* algorithm with a regression-based heuristic. Palm gestures were used as an example of the sequential data and a quadrocopter was the controlled object. The study includes creation of a conceptual model and practical construction of a system using the GPU to ensure the realtime operation. The results present the classification accuracy of chosen gestures and comparison of the computation time between the CPU- and GPU-based solutions.

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A new approach for discovering top-k sequential patterns based on the variety of items

Sakurai S., Nishizawa M.

Journal of Artificial Intelligence and Soft Computing Research

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2015

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Vol. 5, No. 2

141--153

EN

This paper proposes a method that discovers various sequential patterns from sequential data. The sequential data is a set of sequences. Each sequence is a row of item sets. Many previous methods discover frequent sequential patterns from the data. However, the patterns tend to be similar to each other because they are composed of limited items. The patterns do not always correspond to the interests of analysts. Therefore, this paper tackles on the issue discovering various sequential patterns. The proposed method decides redundant sequential patterns by evaluating the variety of items and deletes them based on three kinds of delete processes. It can discover various sequential patterns within the upper bound for the number of sequential patterns given by the analysts. This paper applies the method to the synthetic sequential data which is characterized by number of items, their kind, and length of sequence. The effect of the method is verified through numerical experiments.

3

Formal model of time point-based sequential data for OLAP-like analysis

Bębel B., Morzy T., Królikowski Z., Wrembel R.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2014

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Vol. 62, nr 2

331--340

EN

Numerous nowadays applications generate huge sets of data, whose natural feature is order, e.g,. sensor installations, RFID devices, workflow systems, Website monitors, health care applications. By analyzing the data and their order dependencies one can acquire new knowledge. However, nowadays commercial BI technologies and research prototypes allow to analyze mostly set oriented data, neglecting their order (sequential) dependencies. Few approaches to analyzing data of sequential nature have been proposed so far and all of them lack a comprehensive data model being able to represent and analyze sequential dependencies. In this paper, we propose a formal model for time point-based sequential data. The main elements of this model include an event and a sequence of events. Measures are associated with events and sequences. Measures are analyzed in the context set up by dimensions in an OLAP-like manner by means of the set of operations. The operations in our model are categorized as: operations on sequences, on dimensions, general operations, and analytical functions.

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How to improve efficiency of analysis of sequential data?

Andrzejewski W., Królikowski Z., Morzy T.

Control and Cybernetics

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2009

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Vol. 38, no 1

107-126

EN

Many of todays database applications, including market basket analysis, web log analysis, DNA and protein sequence analysis utilize databases to store and retrieve sequential data. Commercial database management systems allow to store sequential data, but they do not support efficient querying of such data. To increase the efficiency of analysis of sequential data new index structures need to be developed. In this paper we propose an indexing scheme for non-timestamped sequences of sets, which supports set subsequence queries. Our contribution is threefold. First, we describe the index logical and physical structure, second, we provide algorithms for set subsequence queries utilizing this structure, and finally we perform experimental evaluation of the index, which proves its feasibility and advantages in set subsequence query processing.