• Journal of KIISE:Computer Systems and Theory
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• v.26 no.9
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• pp.1158-1165
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• 1999

조건부 분기 명령어(conditional branch instruction)의 잘못된 분기 예측(branch misprediction)은 프로세서의 성능 향상에 심각한 장애 요인이 되고 있다. 특히 시분할(time-sharing) 시스템과 같이 문맥 교환(context switch)이 발생하는 멀티프로그래밍 환경(multiprogramming environment)에서는 더욱 낮은 분기 예측 정확성(branch prediction accuracy)을 보인다. 본 논문에서는 문맥 교환이 발생하는 멀티프로그래밍 환경에서 높은 분기 예측 정확성을 보이는 중첩 분기 예측표 교환(Overlapped Predictor Table Switch, OPTS) 기법을 소개한다. 분기 예측표(predictor table)를 분할하여 각각의 프로세스(process)에 할당하는 OPTS 기법은 문맥 교환의 영향을 최소화함으로써 높은 분기 예측 정확성을 유지하는 분기 예측 방법이다.Abstract There is wide agreement that one of the most important impediments to the performance of current and future pipelined superscalar processors is the presence of conditional branches in the instruction stream. Accurate branch prediction is required to overcome this performance limitation. Many branch predictors have been proposed to help to alleviate this problem, including the two-level adaptive branch predictor, and more recently, hybrid branch predictor. In a less idealized environment, such as a time-sharing system, code of interest involves context switches. Context switches, even at fairly large intervals, can seriously degrade the performance of many of the most accurate branch prediction schemes. In this study, we measure the effect of context switch on the branch prediction accuracy in various situation and show the feasibility of our new mechanism, OPTS(Overlapped Predictor Table Switch), which save and restore branch history table at every context switch.

• Journal of Korea Multimedia Society
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• v.17 no.8
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• pp.946-952
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• 2014

Mobility prediction is one of hot topics using location history information. It is useful for not only user-level applications such as people finder and recommendation sharing service but also for system-level applications such as hand-off management, resource allocation, and quality of service of wireless services. Most of current prediction techniques often use a set of significant locations without taking into account possible location information changes for prediction. Markov-based, LZ-based and Prediction by Pattern Matching techniques consider interesting locations to enhance the prediction accuracy, but they do not consider interesting location changes. In our paper, we propose an algorithm which integrates the changing or emerging new location information. This approach is based on Active LeZi algorithm, but both of new location and all possible location contexts will be updated in the tree with the fixed depth. Furthermore, the tree will also be updated even when there is no new location detected but the expected route is changed. We find that our algorithm is adaptive to predict next location. We evaluate our proposed system on a part of Dartmouth dataset consisting of 1026 users. An accuracy rate of more than 84% is achieved.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.332-343
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• 2022

To address the problems of the gravitational search algorithm (GSA) in which the population is prone to converge prematurely and fall into the local solution when solving the single-objective optimization problem, a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learning mechanism is introduced into the GSA. The agents keep learning from the excellent agents of the population while they are evolving, thus maintaining the memory and sharing of evolution information, addressing the algorithm's shortcoming in evolution that particle information depends on the current position information only, improving the diversity of the population, and avoiding premature convergence. Second, the sine function is used to map the change of the particle velocity into the position probability to improve the convergence accuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the local optimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmark functions. The simulation results show that the proposed algorithm achieved improved the better performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.958-979
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• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.

• Journal of the Korea Institute of Building Construction
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• v.23 no.3
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• pp.273-284
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• 2023

This study is centered on the combined use of LiDAR(Light Detection and Ranging) and AR(Augmented Reality) technologies during vertical and horizontal frame measurements in construction projects. The intention is to enhance the quality control procedure, elevate accuracy, and curtail manual labor along with time expenditure. Present methods for accuracy inspection in frame construction often grapple with reliability concerns due to subjective interpretation and the scope for human error. This research recommends the application of LiDAR and AR technologies to counter these issues and augment the efficiency of the inspection process, along with facilitating the dissemination of results. The suggested technique involves the collection of 3D point cloud data of the frame utilizing LiDAR and leveraging this data for checks on construction accuracy. Furthermore, the inspection outcomes are fed into a BIM (Building Information Modeling) model, and the results are visualized via AR. Upon juxtaposing this methodology with the current approach, it is evident that it offers benefits in terms of objective inspection, speed, precise result sharing, and potential enhancements to the overall quality and productivity of construction projects.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.138-144
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• 2020

Currently, an autonomous vehicle studies are working to develop a four-level autonomous vehicle that can cope with emergencies. In order to flexibly respond to an emergency, the autonomous vehicle must move in a direction to minimize the damage, which must be conducted by judging all the states of the road, such as the surrounding pedestrians, road conditions, and surrounding vehicle conditions. Therefore, in this paper, we suggest a passenger detection and sharing system to detect the passenger situation inside the autonomous vehicle and share it with V2V to the surrounding vehicles to assist in the operation of the autonomous vehicle. Passenger detection and sharing system improve the weighting method that recognizes passengers in the current vehicle to identify the passenger's position accurately inside the vehicle, and shares the passenger's position of each vehicle with other vehicles around it in case of emergency. So, it can help determine the driving of a vehicle. As a result of the experiment, the body pressure sensor applied to the passenger recognition sub-module showed about 8% higher accuracy than the conventional resonant sensor and about 17% higher than the piezoelectric sensor.

• Korean Journal of Applied Biomechanics
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• v.29 no.3
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• pp.157-166
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• 2019

Objective: This study examined the effects of stimulating fingertip temperature on the patterns of force sharing and stability properties during multi-finger force production tasks. Method: 9 adult subjects (male: 3, female: 6, age: $26.11{\pm}4.01yrs$, height: $169.22{\pm}5.97cm$, weight: $61.44{\pm}11.27kg$) participated in this study. The experiment consisted of three blocks: 1) maximal voluntary contraction (MVC) task, 2) single-finger ramp task to quantify enslaving (i.e., unintended force production by non-task fingers), and 3) 12 trials of multi-finger steady-state force production task at 20% MVC. There were three temperature conditions including body-temperature (i.e., control condition), $40^{\circ}C$, and $43^{\circ}C$, and the stimulation was given to the index finger only for all experimental conditions. Results: There were no significant differences in the MVC forces, enslaving, and the accuracy of performance during the steady-state task between the conditions. However, the share of stimulated index finger force increased with the index fingertip temperature, while the share of middle finger force decreased. Also, the coefficient of variation of both index and middle finger forces over repetitive trials increased with the index fingertip temperature. Under the framework of the uncontrolled manifold (UCM) hypothesis used to quantify indices of multi-finger synergies (i.e., stability property) stabilizing total force during the steady-state task, the two variance components within the UCM analysis increased together with the fingertip temperature, while no changes in the synergy indices between the conditions. Conclusion: The current results showed that fingertip temperature stimulation only to index finger does not affect to muscle force production capability of multi-finger, independence of individual fingers, and force production accuracy by the involvement of all four fingers. The effect of fingertip temperature on the sharing pattern and force variation may be due to diffuse reflex effects of the induced afferent activity on alpha-motoneuronal pools. However, the unchanged stability properties may be the reflection of the active error compensation strategies by non-stimulated finger actions.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.9-17
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• 2017

In this paper, we propose a method to develop a multimeter which is used for measuring electric signals in industrial field by using battery and display of smart phone. Most of the multimeters that are linked to existing smartphones are connected to Bluetooth, which is inconvenient for battery use and low in measurement accuracy. To improve this, we developed smart multimeter that can supply power and data at the same time by USB OTG method which can be used by sharing the power of smartphone. We also developed a smart multimeter that can measure resistance, DC voltage, current, AC voltage and current. Developed an Android app with a button that displays measurement data and can select the type of electric signal to be measured, and tests whether the manufactured system is operating normally or not. Respectively.The results of this study confirm that it is possible to fabricate a smart meter that has equal or better performance compared to existing instruments and does not require a separate power source or display, and it is expected to be a reference for the development of various smart instruments in the future.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.4
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• pp.360-368
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• 2017

This paper proposes the 80-kW high-efficiency bidirectional hybrid SiC boost/buck converter using droop control for DC nano-grid. The proposed converter consists of four 20-kW modules to achieve fault tolerance, ease of thermal management, and reduced component stress. Each module is constructed as a cascaded structure of the two basic bi-directional converters, namely, interleaved boost and buck converters. A six-pack hybrid SiC intelligent power module (IPM) suitable for the proposed cascaded structure is adopted for high-efficiency and compactness. The proposed converter with hybrid switching method reduces the switching loss by minimizing switching of insulated gate bipolar transistor (IGBT). Each module control achieves smooth transfer from buck to boost operation and vice versa, since current controller switchover is not necessary. Furthermore, the proposed parallel control using DC droop with secondary control, enhances the current sharing accuracy while well regulating the DC bus voltage. A 20-kW prototype of the proposed converter has been developed and verified with experiments and indicates a 99.3% maximum efficiency and 98.8% rated efficiency.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.373-383
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• 2015

Spatial information has been widely used for efficient land use and management, disaster management, environment management, infrastructure management, corporate marketing, and cultural assets management, and the need for spatial information is expected to be increased. For this reason, central government, local government and public institutions must establish a National Spatial Information System (Fifteen systems related to spatial information managed by National Spatial Data Infrastructure Policy office, NSIS) framework that guarantees high accuracy and quality. The NSIS will provide convenience usage of spatial information in the field of decision-making or civil support. However the current National Spatial Information System is mainly established with separate processes, which causes data redundancy, deterioration of information, passive opening, and sharing of the spatial data. This study suggests 4 standards, which has been derived by applying value-chain model to NSIS data flow, and they are ‘Production and Establishment’, ‘Integration and Sharing’, ‘Application and Fusion’ and ‘Release and Opening’. Based on these standards, the 15 NSIS were analyzed to draw out implications and reforming directions were suggested. By following these suggestions we expect more recent, consist, accurate, and connected National Spatial Information Service which will be more open to public and then satisfy the demands.