• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.242-250
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• 2019

In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.941-955
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• 2015

In dual-channel networks (DCNs), all frequency hopping (FH) sequences used for data channels are chosen from the original FH sequence used for the control channel by shifting different initial phases. As the number of data channels increases, the hitting frequency point problem becomes considerably serious because DCNs is non-orthogonal synchronization network and FH sequences are non-orthogonal. The increasing severity of the hitting frequency point problem consequently reduces the resource utilization efficiency. To solve this problem, we propose a novel hitting frequency point collision avoidance method, which consists of a sequence-selection strategy called sliding correlation (SC) and a collision avoidance strategy called keeping silent on hitting frequency point (KSHF). SC is used to find the optimal phase-shifted FH sequence with the minimum number of hitting frequency points for a new data channel. The hitting frequency points and their locations in this optimal sequence are also derived for KSHF according to SC strategy. In KSHF, the transceivers transmit or receive symbol information not on the hitting frequency point, but on the next frequency point during the next FH period. Analytical and simulation results demonstrate that unlike the traditional method, the proposed method can effectively reduce the number of hitting frequency points and improve the efficiency of the code resource utilization.

• Interdisciplinary Bio Central
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• v.4 no.4
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• pp.14.1-14.6
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• 2012

Splice site prediction in DNA sequence is a basic search problem for finding exon/intron and intron/exon boundaries. Removing introns and then joining the exons together forms the mRNA sequence. These sequences are the input of the translation process. It is a necessary step in the central dogma of molecular biology. The main task of splice site prediction is to find out the exact GT and AG ended sequences. Then it identifies the true and false GT and AG ended sequences among those candidate sequences. In this paper, we survey research works on splice site prediction based on support vector machine (SVM). The basic difference between these research works is nucleotide encoding technique and SVM kernel selection. Some methods encode the DNA sequence in a sparse way whereas others encode in a probabilistic manner. The encoded sequences serve as input of SVM. The task of SVM is to classify them using its learning model. The accuracy of classification largely depends on the proper kernel selection for sequence data as well as a selection of kernel parameter. We observe each encoding technique and classify them according to their similarity. Then we discuss about kernel and their parameter selection. Our survey paper provides a basic understanding of encoding approaches and proper kernel selection of SVM for splice site prediction.

• Korean Management Science Review
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• v.13 no.1
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• pp.13-27
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• 1996

This paper considers the sequencing problem in mixed model assembly lines with hybrid workstation types and sequence-dependent setup times. Computation time is often a critical factor in choosing a method of determining the sequence. We develop a mathematical formulation of the problem to minimize the overall length of a line, and present a tabu search technique which can provide a near optimal solution in real time. The proposed technique is compared with a genetic algorithm and a branch-and-bound method. Experimental results are reported to demonstrate the efficiency of the technique.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.2
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• pp.65-74
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• 1993

In this paper, a two-echelon production-inventory model is developed which integrates the production scheduling problem of the multi-products produced on a single facility and the inventory problem of the related raw materials. The setup costs of the final products are assumed to be dependent on the production sequence. The aim is to determine simultaneously the production cycle time and the production sequence of the final products, and the procurement cycle times of the raw materials. For the model developed, a solution algorithm is suggested and illustrated with a numerical example. And the result is compared with those obtained by two separate subproblems.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1554-1574
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• 2017

This paper introduces a new type of determining factor for Pseudo Random Strings (PRS). This classification depends upon a mathematical property called Finite Induction (FI). FI is similar to a Markov Model in that it presents a model of the sequence under consideration and determines the generating rules for this sequence. If these rules obey certain criteria, then we call the sequence generating these rules FI a PRS. We also consider the relationship of these kinds of PRS's to Good/deBruijn graphs and Linear Feedback Shift Registers (LFSR). We show that binary sequences from these special graphs have the FI property. We also show how such FI PRS's can be generated without consideration of the Hamiltonian cycles of the Good/deBruijn graphs. The FI PRS's also have maximum Shannon entropy, while sequences from LFSR's do not, nor are such sequences FI random.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.139-146
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• 2015

This paper presents the solution to model matching of switched asynchronous sequential machines by corrective control. We propose a model of switched asynchronous sequential machines, in which the system can have different dynamics of asynchronous machines governed by a pre-determined sequence of switching. The control objective is to derive a corrective control law so that the stable state behavior of the closed-loop system can match that of a prescribed model. A new skeleton matrix is defined to represent the reachability of the switched asynchronous machine, and a novel control scheme is presented that interweaves the switching signal and the corrective control procedure. A design algorithm for the proposed controller is illustrated in a case study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1702-1721
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• 2019

A workflow process (or business process) management system helps to define, execute, monitor and manage workflow models deployed on a workflow-supported enterprise, and the system is compartmentalized into a modeling subsystem and an enacting subsystem, in general. The modeling subsystem's functionality is to discover and analyze workflow models via a theoretical modeling methodology like ICN, to graphically define them via a graphical representation notation like BPMN, and to systematically deploy those graphically defined models onto the enacting subsystem by transforming into their textual models represented by a standardized workflow process definition language like XPDL. Before deploying those defined workflow models, it is very important to inspect its syntactical correctness as well as its structural properness to minimize the loss of effectiveness and the depreciation of efficiency in managing the corresponding workflow models. In this paper, we are particularly interested in verifying very large-scale and massively parallel workflow models, and so we need a sophisticated analyzer to automatically analyze those specialized and complex styles of workflow models. One of the sophisticated analyzers devised in this paper is able to analyze not only the structural complexity but also the data-sequence complexity, especially. The structural complexity is based upon combinational usages of those control-structure constructs such as subprocesses, exclusive-OR, parallel-AND and iterative-LOOP primitives with preserving matched pairing and proper nesting properties, whereas the data-sequence complexity is based upon combinational usages of those relevant data repositories such as data definition sequences and data use sequences. Through the devised and implemented analyzer in this paper, we are able eventually to achieve the systematic verifications of the syntactical correctness as well as the effective validation of the structural properness on those complicate and large-scale styles of workflow models. As an experimental study, we apply the implemented analyzer to an exemplary large-scale and massively parallel workflow process model, the Large Bank Transaction Workflow Process Model, and show the structural complexity analysis results via a series of operational screens captured from the implemented analyzer.

• Plant Biotechnology Reports
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• v.2 no.1
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• pp.59-73
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• 2008

Several aspects of photoperception and light signal transduction have been elucidated by studies with model plants. However, the information available for economically important crops, such as Fabaceae species, is scarce. In order to incorporate the existing genomic tools into a strategy to advance soybean research, we have investigated publicly available expressed sequence tag (EST) sequence databases in order to identify Glycine max sequences related to genes involved in light-regulated developmental control in model plants. Approximately 38,000 sequences from open-access databases were investigated, and all bona fide and putative photoreceptor gene families were found in soybean sequence databases. We have identified G. max orthologs for several families of transcriptional regulators and cytoplasmic proteins mediating photoreceptor-induced responses, although some important Arabidopsis phytochrome-signaling components are absent. Moreover, soybean and Arabidopsis genefamily homologs appear to have undergone a distinct expansion process in some cases. We propose a working model of light perception, signal transduction and response-eliciting in G. max, based on the identified key components from Arabidopsis. These results demonstrate the power of comparative genomics between model systems and crop species to elucidate several aspects of plant physiology and metabolism.