Earticle

본 논문에서는 전력선 통신을 이용하는 전기자동차 충전 시스템에 대해 소개하고 전력선 통신을 이용하는 전기자동차 충전 시스템의 제어 신호에 오류가 발생했을 때 딥 러닝 알고 리즘을 적용하여 오류를 정정하는 방식을 제안한다. 제어 신호의 오류 발견과 정정은 기존의 오류정정부호 기법을 통해 해결할 수 있으나 딥 러닝 기반의 오류정정부호 기법을 이용하여 더욱 효율적으로 오류를 발견하고 정정한다. 그래서 딥 러닝 기반의 오류정정부호 기법에 대 해 소개하며 이 기법을 전력선 통신을 이용하는 전기자동차 충전 시스템에 적용하여 시뮬레 이션을 진행하고 비트 오류율로 성능을 확인하여 딥 러닝 기반의 오류정정부호 기법이 기존 의 기법보다 효율적인지를 판단한다.

In this paper, we introduce an electric vehicle charging system using power line communication and propose a method to correct the error by applying a deep learning algorithm when an error occurs in the control signal of an electric vehicle charging system using power line communication. The error detection and correction of the control signal can be solved through the conventional error correcting code schemes, but the error is detected and corrected more efficiently by using the deep learning based error correcting code scheme. Therefore, we introduce deep learning based error correction code scheme and apply this scheme to electric vehicle charging system using power line communication. we proceed simulation and confirm performance with bit error rate. we judge whether the deep learning based error correction code scheme is more effective than the conventional schemes.

The paper proposes reliable channel for medical uses via Physical Layer. The purpose is to serve medical data transmission from WBAN through the UMTS. This study is focusing on applying coding theory to carry high reliability and robustness, in order to satisfy various QoS requirements of medical data. The aim is to build Medical Network Channel that transmits WBAN medical data through the UMTS. The design of extra channel coding is considered as concatenated techniques. The method applies adaptive outer code with inner UMTS code. The accomplishment of QoS is investigated with numerical results proven under AWGN and Rayleigh fading.

무선 스마트카드는 빌딩 보안 어플리케이션에 주된 역할을 하고 있다. 본 연구는 사용자에게 보다 편리함을 주고 스마트카드 시스템의 성능 개선을 제안한다. 스마트카드는 카드 리더기에 삽입을 하지 않고 장치에서 수센티미터 떨어져도 접근이 허용된다. 적응형 변조와 오류 제어를 통해 전력소비를 제어하는 성능은 필요하다. 본 논문에서는 적응형 리드 솔로몬 코드 기반의 FEC와 링크에서 M계수를 변화시킨 M-FSK변조에 대하여 연구한다. 적응형 변조와 적응형 오류 정정은 다른 다양한 정적스키마와 에너지 효율을 비교한 결과 50% 이상 에너지를 저장한다.

Since the wireless smart card has played a main role in the identification security application for the building access; this research has its purpose to improve the performance of the smart card system and aims to offer more convenient to user. The contactless cards do not require insertion into a card reader and can work up to centimeters away from the reading device. To be able to cope with this performance the controlling of power consumption through the adaptive modulation and error control is needed. This paper addresses a forward error control (FEC) scheme with the adaptive Reed-Solomon code rate and an M-ary frequency shift keying (M-FSK) modulation scheme with the varying symbol size M over the link. The result of comparing energy efficiencies of adaptive error correction and adaptive modulation to other various static schemes shows to save over 50% of the energy consumption.

The study reported deals with investigating the feasibility of control strategy for a serial rigid link manipulator that applies impulsive torques at the joints. The strategy is illustrated for a planar three rigid link manipulator. An impulse-based concept which uses successive torque impulses on rigid link as the controller for motion correction was introduced. This control strategy was tested over the entire trajectory to demonstrate that the tracking error could be reduced effectively. The best condition for minimizing the tracking error with the least impulse input at each joint is investigated by considering one design and one operating parameter. The first was the damping in the system, and the second was the sampling time during operation. The results show that this approach can provide useful guidance for the design and control of robot manipulators that require minimum impulse feedback for accurate tracking.

한–중 법률 번역은 법률 용어의 복잡성, 일관성에 대한 엄격한 요구, 그리고 도메인 특화 이중언어 자원의 부족으로 인해 고유한 어려움을 가진다. 최근 신경망 기계번역(NMT)의 발전으로 전반적인 번역 성능은 향상되었으나, 용어의 신뢰성과 오류 분포 문제는 여전히 해결되지 않아 법률 분야에서의 실제 적용 가능성을 제한하고 있다. 본 연구는 mBART 기반 NMT 모델의 디코딩 단계와 사후편집 단계에 이중언어 법률 용어집을 통합하는 용어 인식 번역 프레임워크를 제안한다. 또한 의미 불일치, 문법 오류, 용어 일탈 등을 포함한 주요 법률 번역 오류를 분류하고 정량화하기 위한 종합적인 오류 분석 체계를 제시한다. 한–중 법률 코퍼스를 이용한 실험 결과, 제안된 방법은 BLEU 및 TER과 같은 기존 평가 지표를 향상시킬 뿐만 아니라, 기준 시스템에 비해 더 높은 용어 정확도와 일관성을 달성함을 보여준다. 이러한 결과는 도메인 특화 용어 통제와 체계적인 오류 분석이 기계 기반 법률 번역의 신뢰성을 향상시키는 데 핵심적임을 시사하며, 사법 및 입법 분야에서 보다 신뢰할 수 있는 활용을 가능하게 한다.

Legal translation between Korean and Chinese poses unique challenges due to the complexity of legal terminology, strict requirements for consistency, and the scarcity of domain-specific bilingual resources. While recent advances in neural machine translation (NMT) have improved overall translation performance, issues of terminology reliability and error distribution remain unresolved, limiting the practical applicability of such systems in legal contexts. This study proposes a terminology-aware translation framework that integrates a bilingual legal glossary into both the decoding and post-editing stages of an mBART-based NMT model. In addition, a comprehensive error analysis framework is introduced to classify and quantify common legal translation errors, including semantic inconsistency, grammatical inaccuracies, and terminological deviations. Experiments on a Korean–Chinese legal corpus demonstrate that the proposed approach not only enhances conventional evaluation metrics such as BLEU and TER, but also achieves higher terminology accuracy and consistency compared with baseline systems. These findings highlight the importance of domain-specific terminology control and systematic error analysis in improving the reliability of machine-assisted legal translation, thereby contributing to more trustworthy applications in judicial and legislative contexts.

medicine system. The mobile medicine system contains (i) blood pressure and body temperature measurement value, (ii) ECG medical signals measured by the electrocardiogram device, (iii) mobile patient's history, (iv) G.729 audio signal, MPEG-4 CCD sensor video signal, and JPEG2000 medical image. By the help of the multi-code CDMA spread spectrum communication system with downlink power control scheme and unequal error protection strategy, it is possible to transmit mobile medicine media and meet the quality of service. Numerical analysis and simulation results show that the system is a well transmission platform in mobile medicine.

This study proposes the compensation method for the mechanical deflection error of a SCARA robot. While most studies on the related subject have dealt with the development of a control algorithm for improvement of robot accuracy, this study presents the control method reflecting the mechanical deflection error which is predicted in advance. The deflection at the end of the gripper of SCARA robot is caused by the self-weights and payloads of Arm 1, Arm 2 and quill. If the deflection is constant even though robot’ posture and payload vary, there may not be a big problem on robot accuracy because repetitive accuracy, that is relative accuracy, is more important than absolute accuracy in robot. The deflection in the end of the gripper varies as robot’ posture and payload change. That’ why the moments ,  and  working on every joint of a robot vary with robot’ posture and payload size. This study suggests the compensation method which predicts the deflection in advance with the variations in robot’ posture and payload using neural network. To do this, I chose the posture of robot and the payloads at random, found the deflections by the FEM analysis, and then on the basis of this data, made compensation possible by predicting deflections in advance successively with the variations in robot’ posture and payload through neural network learning.

본 논문은 사물 지능 통신 환경에서 미디어 다중 채널을 위한 오류 제어에 대해서 제안하였다. 이 시스템은 사물 지능 통신 환경 멀티미디어 컴퓨터 협력 작업을 위한 소프트웨어 복구에 적합하다. 이것은 세션의 진행 과정 중 세션의 미디어 서비스 인스턴스가 비정상적으로 종료되는 경우에 세션의 진행을 중단할 수 있지만 허용하는 한 미디어 서비스 인스턴스를 재 활성화 시켜 사용자에 대한 보호를 하는 경우에 필요하다. 본 논문은 규칙-기반 DEVS 모델링과 시뮬레이션 기법을 사용하면서 사물 지능 통신 기반 컴퓨팅 공동 환경의 오류 복구 시스템의 성능 분석을 설명한다.

This paper suggested an error control for multi-channel running on machine to machine environment. This system is suitable for recovering software fault for multimedia CSCW(Computer Supported Cooperative Works) based on machine to machine environment. It is necessary for the system to be protected by reactivity of media service instance instead of breaking process of session. This paper explains a performance analysis of an error recovery system of M2M based computing collaboration environment using rule-based DEVS modeling and simulation techniques.

디지털 캐로절은 사용자들에게 미디어 동기화 메카니즘을 통하여 미디어 객체 공유를 가능하게 한다. 본 시스템은 공동 작업에 참여한 사용자들이 다른 참여자들에게 같은 뷰로써 공유된 미디어 또는 오류 객체들을 참조할 수 있도록 구축하였다. 본 논문은 EC_DCS의 설계와 구축을 설명한다. EC_DCS는 멀티미디어 협동 작업 환경에서 소프트웨어 오류를 감지, 공유, 복구하기에 적합한 시스템이다. 이 시스템에 의해서 오류를 공유할 수 있다. 멀티미디어 공동 작업 환경의 관점에서 오류 공유는 협동 작업에 참가하는 참가자에게 상호작용적으로 오류를 공유한다.

Our Digital Carousel enables user to share media objects through media synchronization mechanism. We implemented the Digital Carousel so that the users participated in collaborative work may refer shared media or error objects as the same view to others. This paper explains the design and implementation of the EC_DXS(Error Control in Digital Carousel System). EC_DCS is a system that is suitable for detecting sharing and recovering software error based on multimedia CSCW(Computer Supportes Cooperated Work). With error sharing system, a group cooperating users can share me. applications. From the perspective of multimedia collaborative environment, an error application becomes another interactive presentation error is shared with participant engaged in a cooperative work.

통신 기술의 발달과 고성능 무선 단말기들의 출시로 무선 환경에서 비디오 스트리밍 서비스 사용자 수는 빠른 속도로 증가하고 있다. 제한된 네트워크 환경에서 많은 사용자에게 UHD급 화질의 비디오 스트리밍 서비스를 제공하기 위해서는 오류 제어 기술은 필수적이다. 이전에 지연에 민감한 비디오 서비스의 오류 제어를 위해서 packet-level FEC(Forward Error Control)기법이 사용되었지만, 이 기술은 중복 데이터에 의한 네트워크 자원 점유가 크다는 단점이 존재한다. 따라서 본 논문에서는 제한된 대역폭을 가진 네트워크 환경에서 기존 FEC 기법들이 제공하는 비디오 서비스 품질은 유지하면서 FEC 데이터에 의한 네트워크 점유를 줄이는 packet-level FEC 알고리즘을 제안한다. 제안 알고리즘은 비디오 압축 기술의 전송 특성을 이용하여 FEC 중복비율을 조정하며, 실험 결과 이전 기법과 비교하여 약 33% 정도의 전송량을 줄임으로써 네트워크 자원 효율을 향상시킬 수 있다.

Due to the development of the communication technology and the high-performance mobile device, the number of video streaming service user has been increasing. Video streaming services in the restricted network environments need error control to support users for the Ultra High Definition(UHD) video delivery. Packet-level FEC method was used to control the error for delay-sensitive video service, but it has a defect in huge occupation of network resources by redundant packets. In this paper, we proposed a packet-level FEC algorithm which maintains the video quality by previous FEC methods and also reduces the occupation of network resource in the restricted network environments. The proposed algorithm controls the redundant rate with using the delivery characteristic of the video coding technology, and improves the efficiency of network resources by reducing the traffic by around 33% in comparison with the previous FEC methods.

This paper suggests a servo control algorithm to reduce the repeatable tracking error which is not explicitly taken into account in the design of a conventional PID controller of a computer hard disk drive. The robust stability of the repetitive control system with multiplicative modelling error is analyzed, and the controller was implemented using a fixed point DSP(Digital Signal Processor). Experimental results show that the repetitive errors are suppressed effectively by the proposed controller.

Accuracy of a machined part is determined by the relative motion between the cutting tool and the workpiece. One of the important factors which affects the relative motion is the geometric errors of a machine tool. In this study, firstly, geometric errors are measured by laser interferometer, and the positioning error of each control point selected uniformly on the control surface CAD model can be estimated from th oirm shaping model and geometric error data base. Where a form shaping function is derived from the link of homogeneous transformation matrix. Secondly, control points are shifted to the estimated amount of positioning errors. A new control surface is modeled with NURBS(Non Uniform Rational B-Spline) surface approximation to the shifted control points. By generating tool paths to the redesigned control surface, we reduce the machining error quite.

최근 다양한 드론과 항공 무기 체계의 발전으로 정밀 항법 장치의 중요성이 강조되고 있으며, 이러한 무기들은 목표에 정확히 도달하기 위해 고도화된 항법 장치를 사용한다. 그러나, 정밀 항법 장치의 중요한 센서인 관성측정센서 (IMU; inertial measurement unit)의 누적 오차 발생은 목표 타격의 정확도에 중대한 영향을 미친다. 이를 해결하기 위해, 본 연구에서는 무향칼만필터 (UKF; unscented kalman filter)의 예측 단계와 확장칼만필터 (EKF; extended kalman filter)의 업데이트 단계를 결합한 무향확장칼만필터 (UEKF; unscented extended kalman filter)를 최초로 제안한다. 본 연구는 EKF, UKF, 그리고 필터를 적용하지 않는 환경을 대상으로 다양한 실험을 수행하여 기존 필터 알고리즘 대비 최대 62%의 정확도 향상과 최대 21% 더 빠른 실시간 처리 성능을 확인하였다. 이러한 성능 개선은 다양한 항법 무기 체계에서 혁신적인 기여를 할 것으로 기대되며, 향후 더 나은 항법 장치 개발에 중요한 알고리즘으로 활용될 수 있다.

Recent advancements in drone and aerial weapon systems have emphasized the importance of precise navigation systems. Such weapons require sophisticated navigation technology to accurately reach targets. However, accumulated errors in the Inertial Measurement Unit (IMU), a critical sensor in precise navigation systems, significantly impact targeting accuracy. To address this issue, this study introduces a novel Unscented Extended Kalman Filter (UEKF) algorithm, which combines the prediction step of the Unscented Kalman Filter (UKF) with the update step of the Extended Kalman Filter (EKF). Our experiments, conducted across various environments including EKF, UKF, and a no-filter baseline, demonstrate that the UEKF algorithm improves accuracy by up to 62% and achieves up to 21% faster real-time processing performance compared to traditional filter algorithms. This performance enhancement is expected to greatly advance the efficacy of various navigation weapon systems and lay the foundation for developing next-generation precision navigation devices.

5-axis CNC machining now is getting popular because it can deal with complex shapes such as impeller, turbine blade and propeller without additional equipment or process, proving a set of various tool orientations. CAM software related to 5-axis machining is being developed quickly so that users can take advantage of potential capacities of 5-axis machine tools. However, only a few researches can be found in the area of control strategy development for 5-axis machining. This paper proposes a 5-axis cross-coupling control system based on a novel tool orientation error model. The proposed tool orientation error model provides accurate information on the tool orientation error in real time, which in turn enables directly controlling the tool orientation accuracy. The proposed control system also employs a contour error model to calculate the contour error and reflect it in the control as well. The accuracy of the proposed tool orientation error model is verified and the performance of the 5-axis cross-coupling control system in terms of both contouring and tool orientation accuracy is evaluated through computer simulations compared with existing 5-axis control systems.

본 논문은 온도 등 7 가지의 IoT 센서에서 수집된 기상데이터의 각 기상요소에 대하여 품질관리(Quality Control; QC)를 하였다. 또한, 우리는 측정된 값에 오류가 있는 데이터를 기계학습으로 의미있게 추정하는 방법을 제안한다. 수집된 기상데이터를 기본 QC 결과를 바탕으로 오류 데이터를 선형 보간하여 기계학습 QC를 진행하였으며, 기계학습 기법으로는 대표적인 서포트벡터회귀, 의사결정테이블, 다층퍼셉트론을 사용했다. 기본 QC의 적용 유무에 따라 비교해 보았을 때, 우리 는 기본 QC를 거쳐 보간한 기계학습 모델들의 평균절대오차(MAE)가 21% 낮은 것을 확인할 수 있었다. 또한, 기계학습 기법에 따라 비교하여 서포트벡터회귀 모델을 적용하였을 때가, 모든 기상 요소에 대하여 MAE가 평균적으로 다층신경망 은 24%, 의사결정테이블은 58% 낮은 것을 알 수 있었다.

In this paper, quality control (QC) is applied to each meteorological element of weather data collected from seven IoT sensors such as temperature. In addition, we propose a method for estimating the data regarded as error by means of machine learning. The collected meteorological data was linearly interpolated based on the basic QC results, and then machine learning-based QC was performed. Support vector regression, decision table, and multilayer perceptron were used as machine learning techniques. We confirmed that the mean absolute error (MAE) of the machine learning models through the basic QC is 21% lower than that of models without basic QC. In addition, when the support vector regression model was compared with other machine learning methods, it was found that the MAE is 24% lower than that of the multilayer neural network and 58% lower than that of the decision table on average.

At present, weighing control system of concrete mixing station mainly uses the discharge valve opening control. And because of unequal drain holes, uneven materials particle sizes, the changing distance between materials and hopper, the material weighing error is large, the concrete mixing ratio error is also imprecise, and the concrete strength quality and curing time have also been great affected. This system uses the time error control principle. According to the material weight error of the last discharge valve open time, the next open time difference is calculated. The last material weight error is compensated. Many discharges effectively solve the error problems of said weighing system and improve measurement accuracy and the mix proportion of concrete. This control method has been successfully applied to the mixing station control system of PLC reconstruction to ensure the system stable operation and concrete curing time, improve weighing precision and concrete strength quality. It has been proven to improve the system running environment and achieve good economic benefits.

Some new simple control methods of modified function projective synchronization in two chaotic systems are investigated in this paper. Based on Lyapunov method, a general feedback error control law is proposed, which contains only feedback error term and easy to implement in practice. Moreover, an adaptive feedback error control scheme is proposed, in which the feedback gain can be automatically adapted to suitable constant. Numerical example is provided to show the effectiveness of proposed method.

This paper proposes a new adaptive error event control model for smart festival management running on situation-awareness environments by analyzing the window and attributes of the object error event. This paper presents the design of the adaptive error event control model for sharing control, which is running on RCSM (Reconfigurable Context Sensitive Middleware) for smart festival management. RCSM provides standardized communication protocols to interoperate an application with others under dynamically changing situations for the smart festival management.

Permanent magnet synchronous motor (PMSM) can appear chaos phenomenon when PMSM in turn on or turn off. When control chaotic PMSM to zero, the state variables of the system can appear fluctuate which is not the ideal control result. In order to overcome fluctuate in control chaos. This paper propose adaptive backstepping of error compensation to control chaotic PMSM, an error compensation item is developed in the virtual control design of each backstepping step to compensate the effect of unknown error dynamics on system for obtaining smooth process of chaos control. This scheme can eliminate oscillation in course of control chaos. Numerical simulations further test the effectiveness of the theoretical analysis.