• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.283-284
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• 2019

In the last decades patient's suffering with Parkinson's disease is increasing at a rapid rate and as per prediction it will grow more rapidly as old age population is increasing at a rapid rate through out the world. As the performance of wearable sensor based approach reached to a new height as well as powerful machine learning technique provides more accurate result these combination has been widely used for assessment of various neurological diseases. ON state is the state where the effect of medicine is present and OFF state the effect of medicine is reduced or not present at all. Classification of ON/OFF state for the Parkinson's disease is important because the patients could injure them self due to freezing of gait and gait related problems in the OFF state. in this paper wearable sensor based approach has been used to collect the data in ON and OFF state and machine learning techniques are used to automate the classification based on the gait pattern. Supervised machine learning techniques able to provide 97.6% accuracy while classifying the ON/OFF state.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4950-4966
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• 2015

Cloud providers now face the problem of estimating the amount of computing resources required to satisfy a future workload. In this paper, a virtual machine provisioning (VMP) mechanism is designed to adapt workload fluctuation. The arrival rate of forthcoming jobs is predicted for acquiring the proper service rate by adopting an exponential smoothing (ES) method. The proper service rate is estimated to guarantee the service level agreement (SLA) constraints by using a diffusion approximation statistical model. The VMP problem is formulated as a facility location problem. Furthermore, it is characterized as the maximization of submodular function subject to the matroid constraints. A greedy-based VMP algorithm is designed to obtain the optimal virtual machine provision pattern. Simulation results illustrate that the proposed mechanism could increase the average profit efficiently without incurring significant quality of service (QoS) violations.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.127-134
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• 2011

A finite state machine was designed to control user's gaze on the screen when the user is monitoring the. It consists of a set of situations where pupils are gazed and a set of states which decide the gaze on a screen or sleeping. The states were especially classified into main states, pre-states and potential states. The machine uses the situation history, which decide current state using continuous previous situation and current situation, and improves the accuracy to control the gaze on a screen. We implemented the machine with the data which were get using a pupil detection method, and tested the verification of the system with monitoring operations. The experimentation using the method which get date from real images shows advantage of decision whether it is temporary gaze or long-term gaze.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.167-173
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• 2002

The look up table method conventionally applied to control the inner temperature and humidity of a laver drying machine has repeatedly occurred not only laver's damage but also inferior goods since the reaching time at the optimum state takes a long time. In this paper, a fuzzy control theory instead of the look up table was proposed to reduce the reaching time at the optimum state. The proposed method used six input variables and four output variables for the fuzzy control, and a triangle rule for a fuzzifier, The Mandani's min-max method was applied to a fuzzy inference. Also, the mean method of maximum was applied to a defuzzifier. The method applied to the fuzzy controller contributed to reduce the reaching time at the optimum state, and to minimize not only laver's damage but also inferior goods.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.2
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• pp.113-120
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• 1999

One of the important challenges facing control engineers in developing automated machineryis to be able to monitor the machines using remote sensors. Observrs are often used to reconstruct the machine variables of interest. However, conventional observers are unalbe to observe the machine variables when the machine models, upon which the observers are based, have inputs that cannot be measured. Since this is often the case, the authors previsously developed a steady-state optimal state and input observer for time-invariant systems [1], this paper extends that work to time-variant systems. A recursive observer, similar to a Kalman-Bucy filter, is developed . This optimal observer minimizes the trace of the error variance for discrete , linear , time-variant, stochastic systems with unknown inputs.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.389-390
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• 2019

최근 블록체인 기반의 어플리케이션이 증가하고 이들을 위한 스마트 컨트랙트가 설계상 오류로 부적절하게 사용될 가능성이 증대되고 있다. 따라서 스마트 컨트랙트의 설계를 보다 안전하게 지원할 수 있는 방안이 필요한 실정이다. 본 논문에서는 State machine을 이용하여 이더리움 스마트 컨트랙트의 기능사용을 보다 안전하게 지원하기 위한 기법을 제안한다. 제안된 기법은 전체 동작의 흐름의 제어하기 위한 Transition Contract와 각각 상태에 대한 스마트 컨트랙트인 State Contract를 이용하여 스마트 컨트랙트의 동작과정을 제어한다.

• Asia pacific journal of information systems
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• v.31 no.2
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• pp.185-196
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• 2021

The paper aims to analyze and predict sales of liquor in the state of Iowa by applying machine learning algorithms to models built for prediction. We have taken recourse of Azure ML and Spark ML for our predictive analysis, which is legacy machine learning (ML) systems and Big Data ML, respectively. We have worked on the Iowa liquor sales dataset comprising of records from 2012 to 2019 in 24 columns and approximately 1.8 million rows. We have concluded by comparing the models with different algorithms applied and their accuracy in predicting the sales using both Azure ML and Spark ML. We find that the Linear Regression model has the highest precision and Decision Forest Regression has the fastest computing time with the sample data set using the legacy Azure ML systems. Decision Tree Regression model in Spark ML has the highest accuracy with the quickest computing time for the entire data set using the Big Data Spark systems.

• Journal of applied mathematics & informatics
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• v.8 no.1
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• pp.273-283
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• 2001

We introduce sums and joins of T-fuzzy transformation semi-groups and investigate their algebraic structures.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1285-1293
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• 2018

This paper proposes a new type of consequent-pole permanent-magnet (CPPM) machine with stator axial magnetic ring that increases torque capability over a wide speed range and enhances efficiency for the built-in rare-earth permanent magnet synchronous machine used in new energy vehicles. The excitation winding of the CPPM hybrid excitation synchronous machine in the stator is replaced by ferrite magnetic ring to simplify the structure and manufacturing process of the machine. The basic structure and magnetic regulation principle of the proposed machine are introduced and compared with the traditional interior rare-earth permanent magnet synchronous machine and CPPM hybrid excitation synchronous machine. Finally, experimental results of a new type of CPPM synchronous motor prototype with axial magnetic ring are introduced in the paper.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.425-430
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• 1986

This paper introduces the program to develop graphics-oriented, interactive, computer-aided design modules for machine elements which are developing in Mechanical Engineering Department of The Ohio State University in U.S.A. The CAD modules are developed to be efficiently performed within the arena of minicomputer- and personal computer-based computer-aided design. In addition, this paepr deals with the state-of-the-art in computer-aided design.