Earticle

Of the total economic loss caused by disasters, 40% are due to floods and floods have a severe impact on human health and life. So, it is important to monitor the water level of a river and to issue a flood warning during unfavorable circumstances. In this paper, we propose a modified error function to improve a hydrological modeling using a multi-layer perceptron (MLP) neural network. When MLP's are trained to minimize the conventional mean-squared error function, the prediction performance is poor because MLP's are highly tunned to training data. Our goal is achieved by preventing overspecialization to training data, which is the main reason for performance degradation for rare or test data. Based on the modified error function, an MLP is trained to predict the water level with rainfall data at upper reaches. Through simulations to predict the water level of Nakdong River near a UNESCO World Heritage Site "Hahoe Village," we verified that the prediction performance of MLP with the modified error function is superior to that with the conventional mean-squared error function, especially maximum error of 40.85cm vs. 55.51cm.

최근 전 세계적으로 기후변화와 기상이변이 심화됨에 따라 국지성 집중호우와 태풍으로 인한 홍수재해 의 발생 빈도와 강도가 점차 증가하고 있으며, 이로 인한 인명 및 재산 피해가 중요한 사회적 문제로 부각되고 있다. 본 연구에서는 2019년부터 2020년까지 발생한 태풍과 장마전선 등 총 4개의 강우사상을 대상으로 외삽 기반 예측 기법과 에러필드 기반 오차 보정 기법을 적용하여 단기 강우 예측을 수행하고, 이를 지상 우량관측소 자료와 비교함으로써 기법별 예측 성능을 종합적으로 검증하였다. 평가 결과, 외삽 기반 강우 예측 기법은 1 ~ 2시간 리드타임에서는 비교적 높은 예측 정확도를 보였으나, 리드타임이 길어질수록 예측 정확도가 점진적으로 감소하는 경향을 나타냈다. 반면, 에러필드 기반 강우 예측 기법은 외삽 기법의 한계를 보완하는 결과를 보여주었으며, 3시간 리드타임에서도 고강도 강우 구간의 예측 성능을 유지하고 외삽 기법 대비 우수한 성능을 나타냈다. 이를 통해 에러필드 기법은 예측 오차를 공간적으로 보정하고, 리드타임 증가에 따른 불확실성을 완화하는데 효과적임을 확인할 수 있었다.

In recent years, the frequency and intensity of flood disasters caused by localized heavy rainfall and typhoons have been steadily increasing worldwide due to climate change and extreme weather events, resulting in significant human and property losses that have emerged as major social concerns. This study applied an extrapolation-based prediction technique and an error-field-based correction technique to 4 rainfall events, including typhoons and stationary fronts, that occurred between 2019 and 2020. The results showed that the extrapolation-based method achieved relatively high accuracy for lead times of 1–2 hours but exhibited gradually decreasing accuracy as lead time increased. In contrast, the error-field-based method demonstrated improved performance over the extrapolation-based approach, maintaining predictive accuracy even for high-intensity rainfall during 3-hour lead times. These findings confirm that the error-field technique effectively corrects spatial prediction errors and mitigates uncertainty associated with increasing lead time.

A method for form error prediction in side wall machining with a flat end mill is suggested. Form error is predicted directly from the tool deflection without surface generation by cutting edge locus with time simulation. Developed model can predict the surface form error about three hundred times faster than the previous method. Cutting forces and tool deflection are calculated considering tool geometry, tool setting error and machine tool stiffness. The characteristics and the difference of generated surface shape in up milling and down milling are discussed. The usefulness of the presented method is verified from a set of experiments under various cutting conditions generally used in die and mold manufacturing. This study contributes to real time surface shape estimation and cutting process planning for the improvement of form accuracy.

Nowadays, lower gear vibration and noise are necessary for drivers in automotive gearbox, which means that transmission gearbox should be optimized to avoid noise annoyance and fatigue before quantity production. Transmission error (T.E.) is the excitation factor that affects the noise level known as gear whine, and is also the dominant source of noise in the gear transmission system. In this paper, the research background, the definition of T.E. and gear micro-modification were firstly presented, and then different transmission errors of loaded torques for the spur gear pair were studied and compared by a commercial software. It was determined that the optimum gear micro-modification could be applied to optimize the transmission error of the loaded gear pair. In the future, a transmission test rig which is introduced in this paper is about to be used to study the T.E. after gear micro-geometry modification. And finally, the optimized modification can be verified by B&K testing equipment in the semi-anechoic room later.

Thermal displacement is an important issue in machine tool systems. During the last several decades, thermal error compensation technology has significantly reduced thermal distortion error; this success has been attributed to the development of a precise, robust thermal error model. A major advantage of using the thermal error model is instant compensation for the control variables during the modeling process. However, successful application of thermal error modeling requires correct determination of the temperature sensor placement. In this paper, a procedure for predicting thermal-mode-based thermal error is introduced. Based on this thermal analysis, temperature sensors were positioned for multiple heat-source models. The performance of the sensors based on thermal-mode error analysis, was compared with conventional methods through simulation and experiments, for the case of a slide table in a transient state. Our results show that for predicting thermal error the proposed thermal model is more accurate than the conventional model.

This paper deals with thermal characteristic analysis of induction motors for machine tool spindle for motion error prediction. Firstly, the inverse design of general induction motors for machine tool spindle has been performed by design principles. Characteristics considering VVVF inverter of induction motors were analyzed. Secondary, power loss and thermal characteristics of induction motors analyzed by equivalent thermal resistance model from Motor-CAD S/W. To develop a second-order fitted power-loss distribution model for the constant-torque operating range of the induction motor, we employed the design of experiment and response surface methodology techniques. Finally, the analysis results were experimentally verified, and the validity of the proposed analysis method was confirmed.

South Korea plans to mandate Zero Energy Buildings (ZEB) for all new public buildings by 2025 and for all new buildings by 2030 to address climate change and reduce energy consumption. ZEBs minimize energy usage and produce sufficient energy on-site to achieve net-zero energy consumption, necessitating the use of all available building surfaces for energy production. Building Integrated Photovoltaics (BIPV), integrating solar panels into building surfaces, is gaining attention. While solar systems are typically installed facing south for maximum efficiency, urban environments often prevent all buildings from being south-facing. Research indicating increased global irradiance suggests the feasibility of installing solar panels on north-facing surfaces. Accurate energy production prediction using simulation software like PVsyst is crucial for BIPV feasibility studies. However, existing studies show significant discrepancies between simulated and actual values on north and west-facing surfaces, affecting economic reliability. This study analyzes and corrects these discrepancies by comparing the actual and simulated photovoltaic output of panels installed in all directions, aiming to improve simulation accuracy.

In this study, a new model using artificial neural networks is proposed to improve the thickness error between the plates, which occurs when the rolling conditions change a lot during the thick rolling. The model was developed by using Python, and the input values are the change in the finish rolling temperature between the plates, the change in target tensile strength, the change in target thickness, and the change in rolling force. The new model is 31.76% better than the existing model based on the standard deviation value of the thickness error. This result is expected to reduce quality costs when applied to online models at actual production sites in the future.

In the end milling process, the information of the surface errors plays an important role in adaptive control systems for precision machining. As the measuring accuracy of the surface errors directly matches the control's, it is an important factor for evaluating the performance of the system. In order to obtain the surface errors, the prediction using the cutting force, torque, motor power etc. is frequently practiced owing to the easiness in measurement. In the implementation of the prediction, the information on the cutting depths make it concrete and precise. Actually the axial depth of cut limits the range of the calculation. In general, it is not easy to know the cutting depths due to irregular shape of workpieces, inaccurate positioning of them on the table of machine tool, and machining error in the previous cutting. In addition to, even if cutting depths are informed, it is difficult to match the individual position of the cutter on the varying shape of the work material. This work suggests an algorithm estimating the cutting depths based on cutting force and makes it precise to predict the surface error. The proposed algorithm can be applied in more extensive cutting situations, such as presence of the tool wear, change of the work material hardness, etc.

This paper is concerned with the development of Internet-based robot system controlled on the remote site via the Internet. In order to draw the public attention into this exciting system, we built the simple system by which a robot is moved in response to answer for the given OX quizzes. As the primary research fer Internet-based robot control, this study focuses on the development of user-friendly interface by which a beginner achieves information for a robot on the remote site from the 3D virtual simulator and the real camera image. for the compensation of Internet time delay, position prediction simulator is implemented in the user interface.

Wind power prediction of wind farm plays a decisive role in stable electric power system operation.The BP neural network’s basic principle was introduced, and the numerical weather prediction (NWP) data and power data of wind farm as the training data of BP neural network was selected and trained; a linear regression model about the sample prediction error was presented, which considers the coupling relationship between the individual sample prediction error, the individual sample prediction error of BP neural network was selected as the regression factor, the individual sample prediction result of BP neural network was modified. As the modified prediction results performing, the prediction algorithm of short-term wind power considering the sample prediction error correction, has good self-learning and adaptive ability of BP neural network. It has overcome the shortcoming that the BP neural network has only considered the overall the prediction error of training samples, but without considered the prediction error of individual samples. This has further improved the prediction accuracy of BP neural network.

This paper presents Adaptive methodology to compensate the On Chip Variations (OCV), aging effect and manufacturing uncertainties in sub threshold circuits. “Canary flip-flop (FF),” is used to predict the timing violations. A FSM, 4-bit Counter, in CMOS 90nm technology whose performance is controlled by automatically changing voltage levels (DVS) as per the timing error prediction. In addition, the voltage supply is further scaled down if no timing error is predicted within certain time period to save more power at different PVT conditions. Back end simulation results shows, the design under observation with this technique can compensate process, supply voltage and temperature instability with an efficient power savings of nearly 40% with respect to the conventional worst case design with timing margins approach. Here we also demonstrate how to define the delay of delay chain during the design phase itself.

전력 수배전반 시스템은 외부의 고압 전력을 건물 내에서 사용하는 220V로 변환하여 공급하는 장치이다. 이러한 시스템은 고장의 발생을 예측하고 예방하는 방법이 존재하지 않는다. 본 논문에서는 배전반 내의 전류, 전압, 온도의 센서 데이터 분석을 통해 수배 전반 시스템의 고장을 예측하는 시스템을 설계하고 구현하였다. 고장 예측 시스템을 이용하여 수배전반 시스템의 동작 신뢰도를 높일 수 있다.

A power distribution system supplies power to a building by converting external high voltage power to lower voltage. There is no known system that can predict and prevent errors. In this paper, we designed and implemented an error prediction system that can gather and analyze the internal state data of power distribution systems. The error prediction system can improve operation reliability of power distribution systems.

For the manual transmission gearbox used in the automotive industry, gear meshing transmission error is the main source of noise known as gear whine, and radiated gear whine noise plays an important role in the whole gearbox. Therefore, in order to keep competitive in the automotive market, the NVH performance of transmission gearboxes is increasingly important for automotive manufacturers when a new product is developed. In this paper, in order to achieve an optimized tooth contact pattern, gear tooth modification is applied to make up for the deformation of the teeth owing to load. A five-speed MT gearbox is firstly modeled in RomaxDesign software and the prediction of transmission error under the loaded torques is studied and compared. From the simulation, the transmission error and maximum contact stress are also simulated and compared after tooth modification of the loaded torques. Finally, the simulation results are used to optimize the whole gearbox design and the final gearbox prototype is testified to obtain NVH performance in a semi-anechoic room.