• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.185-195
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• 2009

As most systems get more complicated, system analysis using simulation has been taken notice of. One of the core parts of simulation analysis is validation of a simulation model, and we can identify how well the simulation model represents the real system with this validation process. The difference between input data of two systems has an effect on the comparison between a simulation model and a real system at validation stage, and the result with such difference is not enough to ensure high credibility of the model. Accordingly, in this paper, we construct a model based on Trace-driven simulation which uses identical input data with the real system. On the other hand, to validate a model by each class, not by an unique statistic, we validate the model using a metric transformed from F-measure which estimates performance of a classifier in data mining field. Finally, this procedure enables precise validation process of a model, and it helps modification by offering feedback at the validation phase.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.801-807
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• 2024

Linear motion robots are devices that perform functions such as transferring parts or positioning devices, and require high precision. In companies that develop linear robot application systems, human workers are in charge of quality control and fault diagnosis of linear robots, and the result and accuracy of a fault diagnosis varies depending on the skill level of the person in charge. Recently, there have been many attempts to utilize artificial intelligence to diagnose faults in industrial devices. In this paper, we present a system that automatically diagnoses linear rail and ball screw misalignment of a linear robot using transfer learning. In industrial systems, it is difficult to obtain a lot of learning data, and this causes a data imbalance problem. In this case, a transfer learning model configured by retraining an established model is widely used. The information obtained by using an acceleration sensor and torque sensor was used, and its usefulness was evaluated for each case. After converting the signal obtained from the sensor into a spectrogram image, the type of abnormality was diagnosed using an image recognition artificial intelligence classifier. It is expected that the proposed method can be used not only for linear robots but also for diagnosing other industrial robots.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2214-2229
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• 2024

Wireless sensor network (WSN) consists of large number of sensor nodes that are deployed in geographical locations to collect sensed information, process data and communicate it to the control station for further processing. Due the unfriendly environment where the sensors are deployed, there exist many possibilities of malicious nodes which performs malicious activities in the network. Therefore, the security threats affect performance and life time of sensor networks, whereas various security aspects are there to address security issues in WSN namely Cryptography, Trust Management, Intrusion Detection System (IDS) and Intrusion Prevention Systems (IPS). However, IDS detect the malicious activities and produce an alarm. These malicious activities exploit vulnerabilities in the network layer and affect all layers in the network. Existing feature selection methods such as filter-based methods are not considering the redundancy of the selected features and wrapper method has high risk of overfitting the classification of intrusion. Due to overfitting, the classification algorithm fails to detect the intrusion in better manner. The main objective of this paper is to provide the efficient feature selection algorithm which was suitable for any type classification algorithm to detect the intrusion in an effective manner. This paper, the security of the network is addressed by proposing Feature Selection Algorithm using Chi Squared with Ensemble Method (FSChE). The proposed scheme employs the combination of decision tree along with the random forest classification algorithm to form ensemble classifier. The experimental results justify the feasibility of the proposed scheme in terms of attack detection, packet delivery ratio and time analysis by employing NSL KDD cup data Set. The obtained results shows that the proposed ensemble method increases the overall performance by 10% to 25% with respect to mentioned parameters.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.265-272
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• 2020

In the research of brain computer interface (BCI) technology, one of the big problems encountered is how to deal with some people as called the BCI-illiteracy group who could not control the BCI system. To approach this problem efficiently, we investigated a kind of spectral EEG characteristics in the prior resting state in association with BCI performance in the following BCI tasks. First, spectral powers of EEG signals in the resting state with both eyes-open and eyes-closed conditions were respectively extracted. Second, a convolution neural network (CNN) based binary classifier discriminated the binary motor imagery intention in the BCI task. Both the linear correlation and binary prediction methods confirmed that the spectral EEG characteristics in the prior resting state were highly related to the BCI performance in the following BCI task. Linear regression analysis demonstrated that the relative ratio of the 13 Hz below and above the spectral power in the resting state with only eyes-open, not eyes-closed condition, were significantly correlated with the quantified metrics of the BCI performance (r=0.544). A binary classifier based on the linear regression with L1 regularization method was able to discriminate the high-performance group and low-performance group in the following BCI task by using the spectral-based EEG features in the precedent resting state (AUC=0.817). These results strongly support that the spectral EEG characteristics in the frontal regions during the resting state with eyes-open condition should be used as a good predictor of the following BCI task performance.

• Journal of KIISE:Software and Applications
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• v.41 no.7
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• pp.481-491
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• 2014

The power production using renewable energy is more important because of a limited amount of fossil fuel and the problem of global warming. A concentrative photovoltaic system comes into the spotlight with high energy production, since the rate of power production using solar energy is proliferated. These systems, however, need to sophisticated tracking methods to give the high power production. In this paper, we propose a hierarchical tracking system using modular Bayesian networks and a naive Bayes classifier. The Bayesian networks can respond flexibly in uncertain situations and can be designed by domain knowledge even when the data are not enough. Bayesian network modules infer the weather states which are classified into nine classes. Then, naive Bayes classifier selects the most effective method considering inferred weather states and the system makes a decision using the rules. We collected real weather data for the experiments and the average accuracy of the proposed method is 93.9%. In addition, comparing the photovoltaic efficiency with the pinhole camera system results in improved performance of about 16.58%.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.137-152
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• 2004

Assesment of 30 nutritional minerals and 8 toxic elements in hair are very important not only for determining adequacy, deficiencies and unbalance, but also for assessing their relative relationships in the body. A test has been developed that serves this purpose exceedingly well. This test is known as tissue mineral analysis(TMA). TMA is very popular method in hair mineral analysis for health care professionals in over 46 countries' medical center. However, there are some problems. First, they do not have database which is suitable for korean to do analyze. Second, as the TMA results from TEI-USA is composed of english documents and graphic files prohibited to open, its usability is very low. Third, some of them has low level database which is related to TMA, so hairs are sent to TEI-USA for analyzing and medical services. it bring about an severe outflow of dollars. Finally, TMA results are based on the database of american health and mineral standards, it is possibly mislead korean mineral standards. The purposes of this research is to develope the first Intelligent Medical Expert System(IMES) of TMA, in Korea, which makes clear the problems mentioned earlier IMES can analyze the tissue mineral data with multiple stage decision tree classifier. It is also constructed with multiple fuzzy rule base and hence analyze the complex data from Korean database by fuzzy inference methods. Pilot test of this systems are increased of business efficiency and business satisfaction 86% and 92% respectively.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.345-349
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• 2009

Social bookmarking systems are a typical web 2.0 service based on folksonomy, providing the platform for storing and sharing bookmarking information. Spammers in social bookmarking systems denote the users who abuse the system for their own interests in an improper way. They can make the entire resources in social bookmarking systems useless by posting lots of wrong information. Hence, it is important to detect spammers as early as possible and protect social bookmarking systems from their attack. In this paper, we applied a diverse set of machine learning approaches, i.e., decision tables, decision trees (ID3), $na{\ddot{i}}ve$ Bayes classifiers, TAN (tree-augment $na{\ddot{i}}ve$ Bayes) classifiers, and artificial neural networks to this task. In our experiments, $na{\ddot{i}}ve$ Bayes classifiers performed significantly better than other methods with respect to the AUC (area under the ROC curve) score as veil as the model building time. Plausible explanations for this result are as follows. First, $na{\ddot{i}}ve$> Bayes classifiers art known to usually perform better than decision trees in terms of the AUC score. Second, the spammer detection problem in our experiments is likely to be linearly separable.