• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.27 no.3
• /
• pp.193-210
• /
• 1991

For the deckhouse or superstructure, attention is directed to the reduction of vibration from a human susceptibility point of view. The two basic requirements for obtaining a low vibration level in the accommodation are to ensure that excitation forces from propeller and/or main engine are small and to avoid resonance excitation of the hull and superstructure. In recent years increased attention has been directed towards the problems of vibration and noise in deckhouse, which have caused major problems with regard to the environmental quality in the living quarters for crews. Accordingly, in this paper, the characteristic of the vibration of deckhouse of fishing boat, of which the length/height ratio is also relatively high, are studied systematically with regard to the shape and modelling of deckhouse based on finite element method of 1-dimensional, 2-dimensional and 3-dimensional model. This study is divided into 4-part. 1st part is the global deckhouse vibration, 2nd part is the local deckhouse vibration, 3rd part consists of the estimation for stiffness of foundational support and 4th part is the application to TUNA LONG LINER of 416 ton class. For the global vibration analysis, the severity of the vibration depends on the longitudinal shear and bending stiffness of the deckhouse, on the vertical deckhouse support(fore, aft and sides). However, even if the design is technically sound, vibration problems may arise due to vertical or longitudinal hull girder or afterbody resonances. Author applied the method of this study to the analysis of, deep-sea fishing vessel of G.T. 416 ton class with relatively low height and long deckhouse, and investigated the vibrational characteristic of the fishing vessel with earlier structural feature. According to this investigation, the vibration, response of above vessel was confirmed of which main hull and deckhouse behave as one body. It is at the bottom of vibrational trouble which a accommodation part of the fishing vessel is raised, that is the local vibration for side wall, fore-aft wall and deck plate of deckhouse rather than thief fect of fore-aft vibration of deckhouse for above fishing vessel. and the resonance of main hull, deckhouse and driving system such as the main engine, propeller in exciting source is mainly brought up as the trouble.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.2 no.1
• /
• pp.35-46
• /
• 1998

Structural damage is used to be modeled through reductions in the stiffness of structural elements for the purpose of damage estimation of structural system. In this study, the concept of joint damage is employed for more realistic damage assessment of a steel structure. The joint damage is estimated damage based on the mode shape informations using neural networks, The beam-to-column connection in a steel frame structure is represented by a rotational spring at the fixed end of a beam element. The severity of joint damage is defined as the reduction ratio of the connection stiffness with respect to the value of the intact joint. The concept of the substructural identification is used for the localized damage assessment in a large structure. The feasibility of the proposed method is examined using an example with simulated data. It has been found that the joint damages can be reasonably estimated for the case with the measurements of the mode vectors subjected to noise.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.123-130
• /
• 2022

During the past decades, detection of gear defects remains as a major problem, especially when the gears are subject to non-stationary phenomena. The idea of this paper is to mixture a multilevel wavelet transform with a fast EMD decomposition in order to early detect gear defects. The sensitivity of a kurtosis is used as an indicator of gears defect burn. When the gear is damaged, the appearance of a crack on the gear tooth disrupts the signal. This is due to the presence of periodic pulses. Nevertheless, the existence of background noise induced by the random excitation can have an impact on the values of these temporal indicators. The denoising of these signals by multilevel wavelet transform improves the sensitivity of these indicators and increases the reliability of the investigation. Finally, a defect diagnosis result can be obtained after the fast transformation of the EMD. The proposed approach consists in applying a multi-resolution wavelet analysis with variable decomposition levels related to the severity of gear faults, then a fast EMD is used to early detect faults. The proposed mixed methods are evaluated on vibratory signals from the test bench, CETIM. The obtained results have shown the occurrence of a teeth defect on gear on the 5th and 8th day. This result agrees with the report of the appraisal made on this gear system.

• Smart Structures and Systems
• /
• v.30 no.6
• /
• pp.673-686
• /
• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• Structural Engineering and Mechanics
• /
• v.86 no.6
• /
• pp.715-730
• /
• 2023

Broad studies have addressed the issue of structural element damage identification, however, rubber bearing, as a key component of load transmission between the superstructure and substructure, is essential to the operational safety of a bridge, which should be paid more attention to its health condition. However, regarding the limitations of the traditional bearing damage detection methods as well as few studies have been conducted on this topic, in this paper, inspired by the model updating-based structural damage identification, a two-stage bearing damage identification method has been proposed. In the first stage, we deduce a novel bearing damage localization indicator, called element relative MSE, to accurately determine the bearing damage location. In the second one, the prior knowledge of bearing damage localization is combined with sailfish optimization (SFO) to perform the bearing damage estimation. In order to validate the feasibility, a numerical example of a 5-span continuous beam is introduced, also the noise robustness has been investigated. Meanwhile, the effectiveness and engineering applicability are further verified based on an experimental simply supported beam and actual engineering of the I-40 Bridge. The obtained results are good, which indicate that the proposed method is not only suitable for simple structures but also can accurately locate the bearing damage site and identify its severity for complex structure. To summarize, the proposed method provides a good guideline for the issue of bridge bearing detection, which could be used to reduce the difficulty of the traditional bearing failure detection approach, further saving labor costs and economic expenses.

• Smart Structures and Systems
• /
• v.31 no.1
• /
• pp.57-68
• /
• 2023

Bridge hangers, such as those in suspension and cable-stayed bridges, suffer from cumulative fatigue damage caused by dynamic loads (e.g., cyclic traffic and wind loads) in their service condition. Thus, the identification of damage to hangers is important in preserving the service life of the bridge structure. This study develops a new method for condition assessment of bridge hangers. The tension force of the bridge and the damages in the element level can be identified using the Bayesian optimization method. To improve the number of observed data, the additional mass method is combined the Bayesian optimization method. Numerical studies are presented to verify the accuracy and efficiency of the proposed method. The influence of different acquisition functions, which include expected improvement (EI), probability-of-improvement (PI), lower confidence bound (LCB), and expected improvement per second (EIPC), on the identification of damage to the bridge hanger is studied. Results show that the errors identified by the EI acquisition function are smaller than those identified by the other acquisition functions. The identification of the damage to the bridge hanger with various types of boundary conditions and different levels of measurement noise are also studied. Results show that both the severity of the damage and the tension force can be identified via the proposed method, thereby verifying the robustness of the proposed method. Compared to the genetic algorithm (GA), particle swarm optimization (PSO), and nonlinear least-square method (NLS), the Bayesian optimization (BO) performs best in identifying the structural damage and tension force.

• Journal of Korean Critical Care Nursing
• /
• v.16 no.2
• /
• pp.1-14
• /
• 2023

Purpose : Sleep disturbances in patients in the intensive care unit (ICU) are related to health problems after discharge. Therefore, active prevention and management are required. Hence, identification of the factors that affect sleep in patients who are critically ill is necessary. Methods : The PubMed, Cochrane Library, CINAHL, EMBASE, and Web of Science databases were searched. Selection criteria were observational and experimental studies that assessed sleep as an outcome, included adult patients admitted to the ICU, and published between November 2015 and April 2022. Results : A total of 21,136 articles were identified through search engines and manual searches, and 42 articles were selected. From these, 22 influencing factors and 11 interventions were identified. Individual factors included disease severity, age, pain, delirium, comorbidities, alcohol consumption, sex, sleep disturbance before hospitalization, chronic obstructive pulmonary disease (COPD), cardiovascular disease, and high diastolic blood pressure (DBP), low hemoglobin (Hb), and low respiratory rate (RR). Environmental factors included light level, noise level, and temperature. Furthermore, treatment-related factors included use of sedatives, melatonin administration, sleep management guidelines, ventilator application, nursing treatment, and length of ICU stay. Regarding sleep interventions, massage, eye mask and earplugs, quiet time and multicomponent protocols, aromatherapy, acupressure, sounds of the sea, adaptive intervention, circulation lighting, and single occupation in a room were identified. Conclusion : Based on these results, we propose the development and application of various interventions to improve sleep quality in patients who are critically ill.

• Journal of IKEEE
• /
• v.27 no.4
• /
• pp.518-523
• /
• 2023

The diagnosis of Inter Turn Short Circuits (ITSC) in induction motors is critical due to the escalating severity of faults resulting from even minor disruptions in the stator windings. However, diagnosing ITSC presents significant challenges due to similarities in noise and losses shared with 3-phase induction motors. Although artificial intelligence techniques have been explored for efficient diagnosis, practical applications heavily rely on model-based methods, necessitating further research to enhance diagnostic performance. This study proposed a diagnostic method applied the Clarke Transformation approach, focusing solely on current components while disregarding changes in rotating flux. Experimental results conducted over a 30-minute period, encompassing both normal and ITSC conditions, demonstrate the effectiveness of the proposed approach, with FAR(False Accept Rates) of 0.2% for normal-to-ITSC FRR(False Rejection Rates) and 0.26% for ITSC-to-normal FRR. These findings underscore the efficacy of the proposed approach.

• Progress in Medical Physics
• /
• v.16 no.4
• /
• pp.192-201
• /
• 2005

Experiments and simulation were done to study the impact of contrast agent when CT scan was used to attenuation correction for PET Images in PET/CT system. Whole body phantom was imaged with various concentration of iodine-based contrast agent using CT. Mathematical emission and transmission density map with liver were made to simulate for whole body FDG Imaging. A variety of factors were estimated, including non-uniform enhancement of contrast agent, concentration and distribution size of contrast agent, noise level, image resolution, reconstruction algorithm, hypo-attenuation of contrast agent, and different time phases for contrast agent. Experimental studies showed that Hounsfield unit depends on the concentration of contrast agent and tube voltage. From the simulation data, contrast agents Introduced artifacts and degraded image quality on the attenuation-corrected PET images. The severity of these effects depends on a variety of factors, including the concentration and distribution size of contrast agent, the noise levels, and the Image resolution. These results Indicated that the impact of contrast agents should be considered with a full understanding of their potential problems in clinical PET/CT images.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
• /
• v.32 no.2
• /
• pp.81-86
• /
• 2021

Background and Objectives Therapies have been reported to treat the glottal gap previously. However, these voice therapies showed the limits because many techniques focused only on one among breathing, resonance and phonation. In addition patients often have difficulties visiting hospital frequently. 'Gliding and humming' is vocal training technique that readjusts total vocal patterns such as breathing, resonance and phonation. This technique can be easily applied during short term sessions. The purpose of this study is to evaluate the efficiency of voice therapy with 'gliding and humming' for patients with glottic gap during short-term treatment sessions. Materials and Method Twenty-three patients with glottal gap were selected. Of all patients, 14 patients had sulcus vocalis and 12 patients had muscle tension dysphonia (MTD). Voice therapies were performed 1.9 sessions in average. GRBAS, jitter, shimmer, noise to harmonic ratio, semitone range, closed quotient_vowel and maximum phonation time were compared before and after the therapies. In addition, changes of glottal gap and MTD severity were evaluated. Results Statistically significant improvement was observed. MTD improvement was observed only among the patients with glottal gap improvement. Also sulcus vocalis group showed the statistically significant improvement. Conclusion 'Gliding and humming' was effective to the patients with glottic gap and sulcus vocalis. Also, among patients who have both glottic gap and MTD, the data suggests that voice therapy for glottic gap also makes improvement in MTD.