• Journal of Arbitration Studies
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• v.32 no.1
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• pp.25-42
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• 2022

The modern arbitration practice recognises the need for a faster and simplified procedural framework for international disputes with fairly low amounts at stake. This has driven several institutions to expand their offer of procedural guidelines with a simplified set of rules that would fit this purpose. Expedited arbitration is increasingly used by parties and is growing in popularity. The basic idea behind establishing expedited arbitration rules is to create the possibility for the parties to a dispute to agree on a simplified and streamlined procedure and to have an arbitration award issued within a short period. The associated cost savings for the parties is another benefit. The importance of developing rules for expedited dispute resolution has recently also been considered by the UNCITRAL Working Group II, in light of the "increasing demand to resolve simple, low-value cases by arbitration" and "the lack of international mechanisms cope with such disputes." As a result, the UNCITRAL 2021 Expedited Arbitration Rules (UNCITRAL EAR) took effect on September 19, 2021. The EAR was adopted by the Commission on 21 July 2021 and, next to UNCITRAL's well-known instruments like the Arbitration Rules (UAR) and the Model Law, represent another chapter in the Commission's impactful work in the field of international arbitration. Overall, the UNCITRAL EAR has great potential to meet the need for more flexible and efficient arbitration proceedings, primarily because they provide the tribunal with strong managerial powers while still leaving room for consultation with the parties. However, parties must remember that not all disputes may be suitable for expedited arbitration, and disputes that are complex or have the possibility of being joint or consolidated may not benefit from simplified procedures and tight deadlines. This article will outline the core features and characteristics of the UNCITRAL EAR.

• Journal of Animal Reproduction and Biotechnology
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• v.37 no.3
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• pp.193-201
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• 2022

Subgroups of sperm which share similar motility features documented in mammals indicate between-subject variations that might be related to fertilizing potential of the respective ejaculates. The objectives of this study were to define subpopulations of motile sperm in Gyr falcon semen using kinematic parameters driven by Computer Assisted Semen Analysis (CASA) and to investigate the subject-related variations in these subpopulations. A total of 24 fresh ejaculates from 6 falcons were used to assign each of the 20473 sperms into 3 subpopulations by a multivariate cluster analysis. The proportion of sperms in different sub-populations were compared among subjects by a generalized linear model and repeatability of sperm frequency in different subpopulations was investigated by corelation analysis. The resulting 3 categories of sperm indicated significant differences in all kinematic parameters (p < 0.05). Subpopulation 1 (15.91%) contained sperms with the highest velocity and progressiveness of movement trajectory while subpopulation 3 (6.4%) included the least progressively motile sperms. Proportion of rapid and medium progressive sperm were consistently higher in the ejaculate of three falcons compared to the two other birds which also had the highest proportion of slow non-progressive sperms (p < 0.05). Respective proportion of sperms in each subpopulations indicated significant repeatability over multiple measurements (p < 0.05). In conclusion, subpopulations of motile sperm in Gyr falcon can be identified using kinematic parameters generated by CASA. Individual differences in the proportion of these subpopulations might have potential application for identifying the males with higher fertilizing capacity.

• Journal of IKEEE
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• v.26 no.3
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• pp.488-493
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• 2022

In manufacturing sites, bearing fault in eletrically driven motors cause the entire system to shut down. Stopping the operation of this environment causes huge losses in time and money. The reason of this bearing defects can be various factors such as wear due to continuous contact of rotating elements, excessive load addition, and operating environment. In this paper, a motor driving environment is created which is similar to the domestic manufacturing sites. In addition, based on the established environment, we propose a dataset for bearing fault detection by collecting changes in vibration characteristics that vary depending on normal and defective conditions. The sensor used to collect the vibration characteristics is Microphone G.R.A.S. 40PH-10. We used various machine learning models to build a prototype bearing fault detection system trained on the proposed dataset. As the result, based on the deep neural network model, it shows high accuracy performance of 92.3% in the time domain and 98.3% in the frequency domain.

• Atmosphere
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• v.32 no.3
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• pp.207-221
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• 2022

In Jeju, on January 23, 2016, a cold surge accompanied by heavy snowfall with the most significant amount of 12 cm was the highest record in 32 years. During this period, the temperature of 850 hPa in January was the lowest in 2016. Notably, in 2016, the average surface temperature of January on the Polar cap was the highest since 1991, and 500 hPa geopotential height also showed the highest value. With this condition, the polar vortex in the northern hemisphere meandered and expanded into the subtropics regionally, covering the Korean Peninsula with very high potential vorticity up to 7 Potential Vorticity Unit. As a result, the strong cold advection, mostly driven by a northerly wind, around the Korean Peninsula occurred at over 2𝜎. Previous studies have not addressed this extreme synoptic condition linked to polar vortex expansion due to the unprecedented Arctic warming. We suggest that the occurrence of a strong Ural blocking event after the abrupt warming of the Barents/Karas seas is a major cause of unusually strong cold advection. With a specified mesoscale model simulation with SST (Sea Surface Temperature), we also show that the warmer SST condition near the Korean Peninsula contributed to the heavy snowfall event on Jeju Island.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.2
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• pp.111-118
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• 2022

An electro-mechanical actuator (EMA) is an actuator that combines an electric motor with a mechanical power transmission elements, and it is suitable for urban air mobility (UAM) in terms of design freedom and maintenance. In this paper, the author presents the research results of the EMA that controls the rotor blade pitch angle of UAM. The actuator is based on an inverted roller screw and controls the blade pitch angle through a two-bar linkage. The dynamic equations for the actuator alone and the blade pitching motion with actuator were derived. For the latter, the equivalent moment of inertia is variable depending on the link angle due to the two-bar linkage. The variations of the equivalent moments of inertia are analyzed and compared in terms of the nut motion and the blade pitch motion. For an example model, the variation of the equivalent moment of inertia of the former is smaller than the latter, so it is judged that the dynamic equations derived from the point of view of the nut motion is suitable for the controller design.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.72-77
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• 2021

The radial distribution of etch rate was analyzed using the ion energy flux model in VHF-CCP. In order to exclude the effects of polymer passivation and F radical depletion on the etching. The experiment was performed in Ar/SF6 plasma with an SF6 molar ratio of 80% of operating pressure 10 and 20 mTorr. The radial distribution of Ar/SF6 plasma was diagnosed with RF compensated Langmuir Probe(cLP) and Retarding Field Energy Analyzer(RFEA). The radial distribution of ion energy flux was calculated with Bohm current times the sheath voltage which is determined by the potential difference between the plasma space potential (measured by cLP) and the surface floating potential (by RFEA). To analyze the etch rate uniformity, Si coupon samples were etched under the same condition. The ion energy flux and the etch rate show a close correlation of more than 0.94 of R² value. It means that the etch rate distribution is explained by the ion energy flux.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Journal of the Korean Society of Safety
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• v.37 no.5
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• pp.80-88
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• 2022

The Korean nuclear industry had developed the SPACE (Safety and Performance Analysis Code for nuclear power plants) code, which adopts a two-fluid, three-field model that is comprised of gas, continuous liquid and droplet fields and has the capability to simulate three-dimensional models. According to the revised law by the Nuclear Safety and Security Commission (NSSC) in Korea, the multiple failure accidents that must be considered for the accident management plan of a nuclear power plant was determined based on the lessons learned from the Fukushima accident. Generally, to improve the reliability of the calculation results of a safety analysis code, verification is required for the separate and integral effect experiments. Therefore, the goal of this work is to verify the calculation capability of the SPACE code for multiple failure accidents. For this purpose, an experiment was conducted to simulate a Control Element Drive Mechanism (CEDM) break with a safety injection failure using the ATLAS test facility, which is operated by Korea Atomic Energy Research Institute (KAERI). This experiment focused on the comparison between the experiment results and code calculation results to verify the performance of the SPACE code. The results of the overall system transient response using the SPACE code showed similar trends with the experimental results for parameters such as the system pressure, mass flow rate, and collapsed water level in component. In conclusion, it can be concluded that the SPACE code has sufficient capability to simulate a CEDM break with a safety injection failure accident.