• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.33-41
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• 2018

The purpose of this study is to develop more accurate and robust emotion prediction neural network (EPNN) model by combining heart rate variability (HRV) and neural network. For the sake of improving the prediction performance more reliably, the proposed EPNN model is based on various types of activation functions like hyperbolic tangent, linear, and Gaussian functions, all of which are embedded in hidden nodes to improve its performance. In order to verify the validity of the proposed EPNN model, a number of HRV metrics were calculated from 20 valid and qualified participants whose emotions were induced by using money game. To add more rigor to the experiment, the participants' valence and arousal were checked and used as output node of the EPNN. The experiment results reveal that the F-Measure for Valence and Arousal is 80% and 95%, respectively, proving that the EPNN yields very robust and well-balanced performance. The EPNN performance was compared with competing models like neural network, logistic regression, support vector machine, and random forest. The EPNN was more accurate and reliable than those of the competing models. The results of this study can be effectively applied to many types of wearable computing devices when ubiquitous digital health environment becomes feasible and permeating into our everyday lives.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.8 s.338
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• pp.43-52
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• 2005

An prescaler which uses PLL(Phase Locked Loop) must satisfy high speed operation and low power consumption. Thus the performance or TSPC(True Single Phase Clocked) D-flip flops which is applied at Prescaler is very important. Power consumption of conventional TSPC D-flip flops was increased with glitches from output and unnecessary discharge at internal node in precharge phase. We proposed a new D-flip flop which reduced two clock transistors for precharge and discharge Phase. With inserting a new PMOS transistor to the input stage, we could prevent from unnecessary discharge in precharge phase. Moreover, to remove the glitch problems at output, we inserted an PMOS transistor in output stage. The proposed flip flop showed stable operations as well as low power consumption. The maximum frequency of prescaler by applying the proposed D-flip flop was 2.92GHz and achieved power consumption of 10.61mw at 3.3V. In comparison with prescaler applying the conventional TSPC D-flip $flop^[6]$, we obtained the performance improvement of $45.4\%$ in the view of PDP(Power-Belay-Product).

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.47-55
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• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.69-76
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• 2016

Current driving capability of MCT (MOS Controlled Thyristor) is determined by turn-off capability of conducting current, that is off-FET performance of MCT. On the other hand, having a good turn-on characteristics, including high peak anode current ($I_{peak}$) and rate of change of current (di/dt), is essential for pulsed power system which is one of major application field of MCTs. To satisfy above two requirements, careful control of on/off-FET performance is required. However, triple diffusion and several oxidation processes change surface doping profile and make it hard to control threshold voltage ($V_{th}$) of on/off-FET. In this paper, we have demonstrated the effect of $V_{th}$ adjustment ion implantation on the performance of MCT. The fabricated MCTs (active area = $0.465mm^2$) show forward voltage drop ($V_F$) of 1.25 V at $100A/cm^2$ and Ipeak of 290 A and di/dt of $5.8kA/{\mu}s$ at $V_A=800V$. While these characteristics are unaltered by $V_{th}$ adjustment ion implantation, the turn-off gate voltage is reduced from -3.5 V to -1.6 V for conducting current of $100A/cm^2$ when the $V_{th}$ adjustment ion implantation is carried out. This demonstrates that the current driving capability is enhanced without degradation of forward conduction and turn-on switching characteristics.

• Journal of KIISE:Databases
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• v.35 no.5
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• pp.421-431
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• 2008

Recently XML is becoming a de facto standard for online data exchange between heterogeneous systems and also the research of streaming XML data filtering comes into the spotlight. Since streaming XML data filtering technique needs rapid matching of queries with XML data, it is required that the query processing should be efficiently performed. Until now, most of researches focused only on partial sharing of path expressions or efficient predicate processing and they were work for time and space efficiency. However, if containment relationship between queries is previously calculated and the lowest level query is matched with XML data, we can easily get a result that high level queries can match with the XML data without any other processing. That is, using this containment technique can be another optimal solution for streaming XML data filtering. In this paper, we suggest an efficient XML data filtering based on prime number indexing and containment relationship between queries. Through some experimental results, we present that our suggested method has a better performance than the existing method. All experiments have shown that our method has a more than two times better performance even though each experiment has its own distinct test purpose.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.23-30
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• 2016

In this study, the heat transfer and pressure drop characteristics were evaluated for multi-tube $CO_2$ water heaters with lengths of 4.5 m and 7.5 m. The evaluation was done using the -NTU method, and the results were compared with experimental data. Water flows through the shell side of the water heater, while $CO_2$ flows through 8 inner tubes. The heater uses a counter-current design to maximize the heat transfer efficiency. The energy balance equation describing the flows of $CO_2$ and water for each node is set up using the section-by-section method. The calculated heat transfer rates agree well with the experimental data within ${\pm}5%$ error. The outlet water temperature decreased linearly with the increase of the water flow rate. The calculated heat transfer rates agreed well with the experimental data within ${\pm}3%$ error. The results show that the heat transfer rate increases almost linearly with the increase of water flow rate or $CO_2$ inlet temperature in both the 4.5-m and 7.5-m water heaters, whereas the water outlet temperature linearly decreases with the increase of the water flow rate. The comparison of the $CO_2$ pressure drop between the calculation and experiment results shows good agreement at the high $CO_2$ flow rate within 5 % error, but the value is about 20 % higher in the experimental pressure drop at the low $CO_2$ flow rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.566-575
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• 2010

In recent, artificial immune system has become an important research direction in the anomaly detection of networks. The conventional artificial immune systems are usually based on the negative selection that is one of the computational models of self/nonself discrimination. A main problem with self and non-self discrimination is the determination of the frontier between self and non-self. It causes false positive and false negative which are wrong detections. Therefore, additional functions are needed in order to detect potential anomaly while identifying abnormal behavior from analogous symptoms. In this paper, we design novel network attack detection and response schemes based on artificial immune system, and evaluate the performance of the proposed schemes. We firstly generate detector set and design detection and response modules through adopting the interaction between dendritic cells and T-cells. With the sequence of buffer occupancy, a set of detectors is generated by negative selection. The detection module detects the network anomaly with a set of detectors and generates alarm signal to the response module. In order to reduce wrong detections, we also utilize the fuzzy number theory that infers the degree of threat. The degree of threat is calculated by monitoring the number of alarm signals and the intensity of alarm occurrence. The response module sends the control signal to attackers to limit the attack traffic.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.615-625
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• 2020

The document-term frequency matrix is a term extracted from documents in which the group information exists in text mining. In this study, we generated the document-term frequency matrix for document classification according to research field. We applied the traditional term weighting function term frequency-inverse document frequency (TF-IDF) to the generated document-term frequency matrix. In addition, we applied term frequency-inverse gravity moment (TF-IGM). We also generated a document-keyword weighted matrix by extracting keywords to improve the document classification accuracy. Based on the keywords matrix extracted, we classify documents using a deep neural network. In order to find the optimal model in the deep neural network, the accuracy of document classification was verified by changing the number of hidden layers and hidden nodes. Consequently, the model with eight hidden layers showed the highest accuracy and all TF-IGM document classification accuracy (according to parameter changes) were higher than TF-IDF. In addition, the deep neural network was confirmed to have better accuracy than the support vector machine. Therefore, we propose a method to apply TF-IGM and a deep neural network in the document classification.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.37-47
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• 2018

The number of capabilities of Internet of Things (IoT) devices will exponentially grow over the next years. These devices may generate a vast amount of time-constrained data. In the context of IoT, data management should act as a layer between the objects and devices generating the data and the applications accessing the data for analysis purposes and services. In addition, most of IoT services will be content-centric rather than host centric to increase the data availability and the efficiency of data delivery. IoT will enable all the communication devices to be interconnected and make the data generated by or associated with devices or objects globally accessible. Also, fog computing keeps data and computation close to end users at the edge of network, and thus provides a new breed of applications and services to end users with low latency, high bandwidth, and geographically distributed. In this paper, we propose Edge-Fog cloud-based Hierarchical Data Delivery ($EFcHD^2$) method that effectively and reliably delivers IoT data to associated with IoT applications with ensuring time sensitivity. The proposed $EFcHD^2$ method stands on basis of fully decentralized hybrid of Edge and Fog compute cloud model, Edge-Fog cloud, and uses information-centric networking and bloom filters. In addition, it stores the replica of IoT data or the pre-processed feature data by edge node in the appropriate locations of Edge-Fog cloud considering the characteristic of IoT data: locality, size, time sensitivity and popularity. Then, the performance of $EFcHD^2$ method is evaluated through an analytical model, and is compared to fog server-based and Content-Centric Networking (CCN)-based data delivery methods.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.105-117
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• 2019

Bitcoin, which can be classified as a cryptocurrency, has attracted attention from various industries because it is an innovative digital currency and the beginning of a Blockchain system. However, as the research on Bitcoin progressed, several security vulnerabilities and possible attacks were analyzed. Among them, the security problem caused by the transparency of the Blockchain database prevents the Blockchain system from being applied to various fields. This vulnerability is further classified as the weak anonymity of participating nodes and privacy problem due to disclosure of transaction details. In recent years, several countermeasures have been developed against these vulnerabilities. In this paper, we first describe the main features of the public and private Blockchain, and explain privacy, unlinkability and anonymity. And, three public Blockchain platforms, Dash, Zcash and Monero which are derived from Bitcoin, and Hyperledger Fabric which is a private Blockchain platform, are examined. And we analyze the operating principles of the protocols applied on each platform. In addition, we classify the applied technologies into anonymity and privacy protection in detail, analyze the advantages and disadvantages, and compare the features and relative performance of the platforms based on the computational speed of the applied cryptographic mechanisms.