• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.126-131
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• 2022

The number of joint disease patients is increasing every year. Currently, the most CPM(Continuous Passive Motion) equipment uses expensive imported equipment, and one CPM equipment is designed to be used only in one joint, medical personnel or hospitals who are the main users of the medical equipment need to have several types of CPMs for joint rehabilitation. To solve this problem, this paper designed a multifunctional joint medical equipment that enables rehabilitation of knee, shoulder, and elbow joints in one CPM equipment and includes general, intensive, and adaptive exercise functions for effective treatment according to the patient's condition. The patient's condition was diagnosed using a load cell and a current sensor. In this paper, effective rehabilitation methods were presented and high reliability and precision of medical equipment was confirmed through experiments using potentiometer, encoder, and PI controller.

• ETRI Journal
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• v.44 no.4
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• pp.560-572
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• 2022

H²RDC (heuristic handover based on RCC-DTSK-C), a heuristic algorithm based on a highly interpretable deep Takagi-Sugeno-Kang fuzzy classifier, is proposed for suppressing the mobile heterogeneous networks problem of frequent handover and handover ping-pong in the multibase-station scenario. This classifier uses a stack structure between subsystems to form a deep classifier before generating a base station (BS) priority sequence during the handover process, and adaptive handover hysteresis is calculated. Simulation results show that H²RDC allows user equipment to switch to the best antenna at the optimal time. In high-BS density load and mobility scenarios, the proposed algorithm's handover success rate is similar to those of classic algorithms such as best connection (BC), self tuning handover algorithm (STHA), and heuristic for handover based on AHP-TOPSIS-FUZZY (H²ATF). Moreover, the handover rate is 83% lower under H²RDC than under BC, whereas the handover ping-pong rate is 76% lower.

• Advances in Energy Research
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• v.8 no.3
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• pp.175-184
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• 2022

The dual-fuel technology, which uses gaseous fuel as the main fuel and liquid as the pilot fuel, is an appealing technology for reducing the exhaust emissions. The current study proposes emission models based on ANFIS for a dual-fuel using producer gas (PG)-diesel engine. Emissions measurements were taken at different engine load levels and fuel injection timings. The proposed model predictions were examined using statistical methods. With R2 values in the range of 0.9903 to 0.9951, the established ANFIS model was found to be consistently robust in predicting emission characteristics. The mean absolute percentage deviate in range 1.9 to 4.6%, and mean squared error varies in range 0.0018 to 13.9%. The evaluation of the ANFIS model developed shows a reliable claim of intrinsic sensitivity, strength, and outstanding generalization. The presented meta-model can be used to simulate the engine's operation in order to create an efficient control tool.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.4
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• pp.2428-2435
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• 2014

Numerical analysis with ALE (Arbitrary Lagrangian Eulerian) Adaptive Meshing technique was performed to evaluate the pullout capacity of the embedded suction anchors (ESA) in uniform clay. The numerical method was verified by the previous study, analytical results based on limit-equilibrium theory and centrifuge tests. The pullout capacity of the ESA under horizontal, vertical, and inclined loading were evaluated, and the effect of initial rotation of the ESA on pullout capacity was also investigated. The analysis results showed that the maximum horizontal capacity was obtained at the mid-point, and the each vertical capacity gave the similar value regardless of the loading points. Furthermore, the inclined capacity was decreased as the load inclination angle increased at the mid-point of the anchor, and almost the same pullout capacity was obtained when the initial rotation angles were below 30 degrees.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.7 no.2
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• pp.36-40
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• 1993

In order to improve complicated construction and complex control which are disadvantage of optimal PWM technique aimed at harmonic elimination method, this paper presented MRA(Model Reference Adaptive) PWM technique that gatmg signal of inverter is generated by comparing the reference signal with the induced feedback signal at the reference model of load. Design of controller is composed of microprocessor and analog circuit. MRA PWM technique used in the paper is able to compensate the degradation of voltage efficiency to be generated by the ratio of the output voltage to the DC supply voltage being low for using conventional sinusoidal PWM technique. When the trapezoidal signal is employed as the reference signal. the low order harmonics of line current can be reduced and the switching pattern is made by on-line computation using comparatively simple numerical analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1307-1312
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• 2015

As smart-phones become popular, mobile data traffic has been dramatically increasing and intensive researches on the next-generation mobile communication network is in progress to meet the increasing demand for mobile data traffic. In particular, heterogeneous network (HetNet) is attracting much interest because it can significantly enhance the network capacity by increasing the spatial reuse with macro and small cells. In the HetNet, we have several problems such as load imbalance and interference because of the difference in transmit power between macro and small cells and cell range expansion (CRE) can mitigate the problems. In this paper, we propose a new cell selection scheme with adaptive cell range expansion bias (CREB) for ultra dense HetNet and we analyze the performance of the proposed scheme in terms of average cell transmission rate through system-level simulations and compare it with those of other schemes.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.6
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• pp.509-516
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• 2002

This paper presents a simple torque estimation method and switching angle control of Switched Reluctance Motor(SRM) using Neural Network(NN). SRM has gaining much interest as industrial applications due to the simple structure and high efficiency. Adaptive switching angle control is essential for the optimal driving of SRM because of the driving characteristic varies with the load and speed. The proper switching angle which can increase the efficiency was investigated in this paper. NN was adapted to regulate the switching angle and nonlinear inductance modelling. Experimental result shows the validity of the switching angle controller.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.4
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• pp.412-418
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• 2011

Reliability-based Topology optimization(RBTO) is to get an optimal design satisfying uncertainties of design variables. Although RBTO based on homogenization and density distribution method has been done, RBTO based on BESO has not been reported yet. This study presents a reliability-based topology optimization(RBTO) using bi-directional evolutionary structural optimization(BESO). Topology optimization is formulated as volume minimization problem with probabilistic displacement constraint. Young's modulus, external load and thickness are considered as uncertain variables. In order to compute reliability index, four methods, i.e., RIA, PMA, SLSV and ADL(adaptive-loop), are used. Reliability-based topology optimization design process is conducted to obtain optimal topology satisfying allowable displacement and target reliability index with the above four methods, and then each result is compared with respect to numerical stability and computing time. The results of this study show that the RBTO based on BESO using the four methods can effectively be applied for topology optimization. And it was confirmed that DLSV and ADL had better numerical efficiency than SLSV. ADL and SLSV had better time cost than DLSV. Consequently, ADL method showed the best time efficiency and good numerical stability.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.103-108
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• 2007

Neural network is used in many fields of control systems currently. However, It is not easy to obtain input-output pattern when neural network is used for the system of a single feedback controller and it is difficult to get satisfied performance with neural network when load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object in place of activation function of Neural Network output node. As the Neural Network self adaptive control system is designed in simple structure neural network input-output pattern problem is solved naturally and real tin Loaming becomes possible through general back propagation algorithm. The effect of the proposed Neural Network self adaptive control algorithm was verified in a test of controlling the speed of a A.C. servo motor equipped with a high speed computing capable DSP (TMS320C32) on which the proposed algorithm was loaded.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.2
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• pp.59-68
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• 2002

In this paper, we compared and analyzed two new models of cyclic packet dropping algorithm, Adaptive Cyclic Packet Dropping algorithm (ACPD), and Non-adaptive Cyclic Packet Dropping algorithm (NCPD) with RIO. The ACPD algorithm drops adaptively packets for the congestion control, as predicting traffic pattern between each cycle. Therefore the ACPD algorithm makes up for the drawback of RIO algorithm and minimizes the wastes of the bandwidth being capable of predicting in the NCPD algorithm. We modelled two cyclic packet drop algorithms and executed a simulation and analyzed the throughput and packet drop rate based on Sending Priority changing dynamically depending on network traffic. In this algorithm, applying the strict drop precedence policy, we get better performance on priority levels. The results show that two new algorithms may provide more efficient and stricter drop precedence policy as compared to RIO independent of traffic load. The ACPD algorithm can provide better performance on priority levels and keep stricter drop policy than other algorithms.