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An efficient approach for Interactive sequential pattern recognition

pattern recognition 2017年 64卷 295-304页

作者： Calvo-Zaragoza, Jorge Oncina, Jose Univ Alicante Dept Lenguajes & Sistemas Informat Carretera San Vicente del Raspeig S-N Alicante 03690 Spain

Interactive pattern recognition (IPR) is an emergent framework in which the user is involved actively in the recognition process by giving feedback to the system when an error is detected. Although this framework is expected to reduce the number of errors to correct, it may increase the time required to complete the task since the machine needs to recompute its proposal after each interaction. Therefore, a fast computation is required to make the interactive system profitable and user-friendly. This work presents an efficient approach to deal with IPR tasks when data has a sequential nature. Our approach includes some computation at the very beginning, of the task but it then achieves a linear complexity after user corrections. We also show how these tasks can be effectively carried out if the solution space is defined with a Regular Language. This fact has indeed proven to'e the most relevant factor to improve the efficiency of the approach. Several experiments are carried out in which our proposal is faced against a classical search. Results show a reduction in time in all experiments considered, solving efficiently some complex IPR tasks thanks to our proposals.

关键词： Interactive pattern recognition sequential pattern recognition Human-computer interaction Word graphs Efficient search

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Frequent Alarm pattern Mining of Industrial Alarm Flood Sequences by an Improved PrefixSpan Algorithm

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PROCESSES 2023年第4期11卷

作者： Yang, Songbai Zhang, Tianxing Zhai, Yingchun Wang, Kaifa Zhao, Guoxi Tu, Yuanfei Cheng, Li Petrochina Tarim Petrochem Co Ltd Korla 841000 Peoples R China Nanjing Tech Univ Control Engn Ctr Nanjing 210037 Peoples R China Kunlun Digital Intelligence Technol Co Ltd Beijing 100007 Peoples R China Hexagons Asset Lifecycle Intelligence Beijing 100026 Peoples R China Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 210037 Peoples R China

Alarm systems are essential to the process safety and efficiency of complex industrial facilities. However, with the increasing size of plants and the growing complexity of industrial processes, alarm flooding is becoming a serious problem and posing challenges to alarm systems. Extracting alarm patterns from an alarm flood database can assist with an alarm root cause analysis, decision support, and the configuration of an alarm suppression model. However, due to the large size of the alarm database and the problem of sequence ambiguity in the alarm sequence, existing algorithms suffer from excessive computational overhead, incomplete alarm patterns, and redundant outputs. In order to solve these problems, we propose an alarm pattern extraction method based on the improved PrefixSpan algorithm. Firstly, a priority-based pre-matching strategy is proposed to cluster similar sequences in advance. Secondly, we improved PrefixSpan by considering timestamps to tolerate short-term order ambiguity in alarm flood sequences. Thirdly, an alarm pattern compression method is proposed for the further distillation of pattern information in order to output representative alarm patterns. Finally, we evaluated the effectiveness and applicability of the proposed method by using an alarm flood database from a real diesel hydrogenation unit.

关键词： alarm management industrial alarm systems alarm flood PrefixSpan algorithm sequential pattern recognition

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pattern mining in alarm flood sequences using a modified PrefixSpan algorithm

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ISA TRANSACTIONS 2019年 90卷 287-293页

作者： Niyazmand, Tahereh Izadi, Iman Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

Proper monitoring of performance of an alarm system throughout its life cycle is an important factor in safety and reliability of industrial plants. Complexity and extent of modern industrial plants and poor design and management of alarm systems, have increased the importance of monitoring of alarm systems. Alarm floods, defined as a large number of alarms triggered in a short interval, is one of the problems that modern complexes are facing regularly. Many researchers have been focusing on this issue both in academia and industry. One approach to deal with alarm flood is analyzing alarms triggered in different floods and finding similar patterns. The identified patterns could help in locating the root cause of an alarm flood. In this paper a modified PrefixSpan sequential pattern recognition algorithm is used to find alarm patterns in different floods. The effectiveness of the algorithm is demonstrated with real alarm floods from a natural gas processing plant. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Alarm flood Alarm management PrefixSpan algorithm sequential pattern recognition

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sequential Memristor Crossbar for Neuromorphic pattern recognition

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IEEE TRANSACTIONS ON NANOTECHNOLOGY 2016年第6期15卷 922-930页

作者： Son Ngoc Truong Khoa Van Pham Yang, Wonsun Min, Kyeong-Sik Kookmin Univ Sch Elect Engn Seoul 136702 South Korea

Most of human's intelligent behaviors such as inference, prediction, anticipation, etc. are based on the processing of sequential data from human's sensory systems. Thus, a sequential memory that can process sequential information is very essential to mimic brain's intelligent behaviors. In this paper, we propose a new sequential memristor crossbar which is regarded as the first memristor circuit that copes with the sequential data. The new crossbar is composed of two layers which are the base layer and the sequential one, respectively. The base layer can recognize only static items one by one. The sequential layer can detect the serial order of items and find the best match with the detected sequence among many reference sequences stored in the memristor array. The new crossbar can recognize the tested sequences of items as well as 88.6% on average for the memristance variation of 0%. The variation tolerance is also tested from 0-% variation to 20-% variation in the proposed sequential crossbar.

关键词： Memristor crossbar neuromorphic circuit pattern recognition sequential memristor crossbar sequential pattern recognition

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SEMI-SUPERVISED AND MULTI-TASK LEARNING FOR ON-STREET PARKING SPACE STATUS INFERENCE

SEMI-SUPERVISED AND MULTI-TASK LEARNING FOR ON-STREET PARKIN...

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International Conference on Multimedia Analysis and pattern recognition (MAPR)

作者： Wu, You-Feng Vu-Hoang Tran Huang, Ching-Chun Natl Chung Cheng Univ Dept Elect Engn Chiayi Taiwan HCMC Univ Technol & Educ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

ISBN: (纸本)9781728118291

To manage on-street parking spaces, magnetic sensor is often used due to its low cost and flexibility in installation and usage. However, its signals are easily affected by environment, vehicle type, installation location and moving neighboring vehicles. Besides, accidental installation also leads to non-unified coordinate of magnetic sensors which makes the management system difficult to recognize. To overcome these challenges, we proposed a novel semi-supervised and multi-task learning framework for sensor based on-street parking slot inference with three contributions. First, a Coordinate Transform Module is integrated into our framework to reduce the diversity of input signals by transforming them adaptively into a unified coordinate. Second, to learn the generalized and discriminative features while minimizing the amount of labeled data, we introduce a Multi-task Module to leverage the information from both labeled and unlabeled data. Third, we embed a Temporal Module, which observes and memorizes the parking states from time to time, to infer parking space status in a reliable way. The experimental results show that, with the proposed three modules, our end-to-end training framework could reduce the error detection and hence improve the system accuracy.

关键词： On-street Vacant Space Detection sequential pattern recognition Deep Learning Network An-isotropic Magnetic Sensors

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Is the Sequence of SuperAlarm Triggers More Predictive Than Sequence of the Currently Utilized Patient Monitor Alarms?

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2017年第5期64卷 1023-1032页

作者： Bai, Yong Do, Duc Ding, Quan Palacios, Jorge Arroyo Shahriari, Yalda Pelter, Michele M. Boyle, Noel Fidler, Richard Hu, Xiao Univ Calif Los Angeles Dept Biomed Engn Los Angeles CA 90095 USA Univ Calif San Francisco Dept Physiol Nursing San Francisco CA 94143 USA Univ Calif Los Angeles David Geffen Sch Med UCLA Cardiac Arrhythmia Ctr Los Angeles CA 90095 USA Univ Calif San Francisco Dept Physiol Nursing & Neurosurg Inst Computat Hlth Sci UCB UCSF Grad Grp Bioengn San Francisco CA 94143 USA

Objective: Our previous studies have shown that "code blue" events can be predicted by SuperAlarm patterns that are multivariate combinations of monitor alarms and laboratory test results cooccurring frequently preceding the events but rarely among control patients. Deploying these patterns to the monitor data streams can generate SuperAlarm sequences. The objective of this study is to test the hypothesis that SuperAlarm sequences may contain more predictive sequential patterns than monitor alarms sequences. Methods: Monitor alarms and laboratory test results are extracted from a total of 254 adult coded and 2213 control patients. The training dataset is composed of subsequences that are sampled from complete sequences and then further represented as fixed-dimensional vectors by the term frequency inverse document frequency method. The information gain technique and weighted support vector machine are adopted to select the most relevant features and train a classifier to differentiate sequences between coded patients and control patients. Performances are assessed based on an independent dataset using three metrics: sensitivity of lead time (Sen(L)@ T), alarm frequency reduction rate (AFRR), and work- up to detection ratio (WDR). Results: The performance of 12-h-long sequences of SuperAlarm can yield a SenL@2 of 93.33%, an AFRR of 87.28%, and a WDR of 3.01. At an AFRR = 87.28%, SenL@2 for raw alarm sequences and discretized alarm sequences are 73.33% and 70.19%, respectively. At a WDR = 3.01, SenL@2 are 49.88% and 43.33%. Conclusion and Significance: The results demonstrate that SuperAlarm sequences indeed outperform monitor alarm sequences and suggest that one can focus on sequential patterns from SuperAlarm sequences to develop more precise patient monitoring solutions.

关键词： Code blue event prediction physiological monitor alarm sequential pattern recognition SuperAlarm

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Sightseeing Plan Recommendation System using sequential pattern Mining based on Adjacent Activities

Sightseeing Plan Recommendation System using Sequential Patt...

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Asian Control Conference

作者： Takuma Fujitsuka Tomohiro Harada Hiroyuki Sato Keiki Takadama Tomohiro Yamaguchi The University of Electro-Communications Nara National College of Technology

ISBN: (纸本)9781479978632

This study proposes the novel sightseeing plan recommendation system based on the adjacency of the activities in the plans (e.g., a shopping is selected after a lunch), and aims at verifying its effectiveness through the subject experiments. The proposed system recommends the sightseeing plans according to the adjacent relationship of the activities in the plans given by the group of other users whose preference are similar to the user. The intensive subject experiments have revealed that our proposed system can recommend the plans that contains the appropriate order of activities for the sightseeing plan recommendation in comparison with ones recommended by the conventional system.

关键词： Planning Recommender systems Neighbor sequential pattern recognition mining Preferences preference

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Efficient Process Discovery From Event Streams Using sequential pattern Mining

Efficient Process Discovery From Event Streams Using Sequent...

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IEEE Symposium Series on Computational Intelligence

作者： Marwan Hassani Sergio Siccha Florian Richter Thomas Seidl Data Management and Data Exploration Group RWTH Aachen University

ISBN: (纸本)9781479975617

Process mining is an emerging research area that brings the well-established data mining solutions to the challenging business process modeling problems. Mining streams of business processes in the real time as they are generated is a necessity to obtain an instant knowledge from big process data. In this paper, we introduce an efficient approach for exploring and counting process fragments from a stream of events to infer a process model using the Heuristics Miner algorithm. Our novel approach, called StrProM, builds prefix-trees to extract sequential patterns of events from the stream. StrProM uses a batch-based approach to continuously update and prune these prefix-trees. The models are generated from those trees after applying a decaying mechanism over their statistics. The extensive experimental evaluation demonstrates the superiority of our approach over a state-of-the-art technique in terms of execution time using a real dataset, while delivering models of a comparable quality.

关键词： streams mining procedural model rivers computational procedure execution time sequential Business sequential pattern recognition

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A Real Time Performance Assessment of Simultaneous pattern recognition Control for Multi-functional Upper Limb Prostheses

A Real Time Performance Assessment of Simultaneous Pattern R...

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International IEEE/EMBS Conference on Neural Engineering

作者： Sophie M. Wurth Levi J. Hargrove Swiss Federal Institute of Technology in Lausanne Department of Physical Medicine and Rehabilitation Northwestern University

ISBN: (纸本)9781467319676

A natural and intuitive operation of multifunctional upper limb prostheses involves the concurrent activation of multiple degrees of freedom in a proportional way. Several approaches to simultaneous and proportional control strategies have been investigated;provided outcome measures however were offline accuracy or error rates and lacked the functional component of a preclinical assessment. This study evaluated a simultaneous proportional pattern recognition control strategy with two parallel classifiers in a two-dimensional Fitts' law style test and compared it to a sequential pattern recognition approach. The proposed test allowed for a complete evaluation through different performance metrics such as throughput (TP, bits/sec), path efficiency (PE,%), completion rate (%), overshoot (%) and reaction time (sec). We found that the simultaneous approach presented with numerous advantages with respect to the sequential alternative through significantly higher TP and PE for combined-motion targets (p<0.001) and significantly less overshooting in both combined and discrete targets (p<0.01). For discrete motions, the TP was significantly lower for the simultaneous approach (p<0.001) but PE was similar. There was no difference in either completion rate or reaction time. These results support the potential of simultaneous pattern recognition for the control of multifunctional prostheses and underline the usefulness of a simple functional test in a preclinical framework.

关键词： Artificial Limbs pattern recognition Degree of freedom Polyethylenes Performance metrics Prosthesis Concurrency functional testing Upper Extremity Reaction Time Error analysis overshooting Performance assessment control strategy sequential pattern recognition

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A production schedule generator framework for pattern sequential mining

A production schedule generator framework for pattern sequen...

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International Conference on Computer Sciences and Convergence Information Technology (ICCIT)

作者： Ruhaizan Ismail Zalinda Othman Azuraliza Abu Bakar Centre for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia

ISBN: (纸本)9781467308946

Scheduling is one of the fields that are crucial in the manufacturing industry. A good schedule improves productivity and may help in making related production decision. This paper proposes a schedule generator framework for mining sequential patterns in the production schedules. The aim of this study is to produce knowledge pattern through a set of sequence rules. This framework consists of a few phases of sequential pattern mining to extract certain patterns or knowledge embedded in schedules. The pattern or knowledge produced by this framework is illustrated in two ways that are comprised of a set of sequence rules and a set of frequent schedules.

关键词： sequential pattern mining framework schedule generator data mining pattern production scheduling production control Frameworks data mining mining sequential pattern recognition Generator dynamos sequential

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