• Annals of Clinical Neurophysiology
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• v.9 no.1
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• pp.1-4
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• 2007

Vertigo is an illusion of rotation, which results from an imbalance within the vestibular system. This review focuses on two common presentations of spontaneous vertigo: acute prolonged spontaneous vertigo and recurrent spontaneous vertigo. Common causes of acute prolonged spontaneous vertigo include vestibular neuritis, labyrinthitis, and brainstem or cerebellar stroke. The history and detailed neurological/neurotological examinations usually provide the key information for distinguishing between peripheral and central causes of vertigo. Brain MRI is indicated in any patient with acute vertigo accompanied by abnormal neurological signs, profound imbalance, severe headache, and central patterns of nystagmus. Recurrent spontaneous vertigo occurs when there is a sudden, temporary, and largely reversible impairment of resting neural activity of one labyrinth or its central connections, with subsequent recovery to normal or near-normal function. Meniere's disease, migrainous vertigo, and vertebrobasilar insufficiency (VBI) are common causes. The duration of the vertigo attack is a key piece of information in recurrent spontaneous vertigo. Vertigo of vascular origin, such as VBI, typically lasts for several minutes, whereas recurrent vertigo due to peripheral inner-ear abnormalities lasts for hours. Screening neurotological evaluations, and blood tests for autoimmune and otosyphilis are useful in assessment of recurrent spontaneous vertigo that are likely to be peripheral in origin.

• Journal of the Korea Society of Computer and Information
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• v.1 no.1
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• pp.129-138
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• 1996

In the result of Industrial development. largeness and highness of techniques. a large amount of Information Is being treated every year. Achive informationization. we must store in computer ,all informations written on paper for a long time and be able to utilize them In right time and place. There Is recurrent neural network as a model rousing the output value In learning neural network for characters recognition. But most of these methods are not so effectively applied to it. This study suggests a new type of recurrent neural network to classifyeffectively the static patterns such as off-line handwritten characters. This study shows that this new type Is better than those of before in recognizing the patterns. such as figures and handwritten characters, by using the new J-E (Jordan-Elman) neural network model in which enlarges and combines Jordan and Elman Model.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.11
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• pp.1464-1471
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• 1999

This paper introduces complex-valued competitive learning neural network for spatio-temporal pattern recognition. There have been quite a few neural networks for spatio-temporal pattern recognition. Among them, recurrent neural network, TDNN, and avalanche model are acknowledged as standard neural network paradigms for spatio-temporal pattern recognition. Recurrent neural network has complicated learning rules and does not guarantee convergence to global minima. TDNN requires too many neurons, and can not be regarded to deal with spatio-temporal pattern basically. Grossberg's avalanche model is not able to distinguish long patterns, and has to be indicated which layer is to be used in learning. In order to remedy drawbacks of the above networks, unsupervised competitive learning using complex umber is proposed. Suggested neural network also features simultaneous recognition, time-shift invariant recognition, stable categorizing, and learning rate modulation. The network is evaluated by computer simulation with randomly generated patterns.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.2
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• pp.534-540
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• 1996

This paper presents an Adaptive Time-Delay Recurrent Neural Network (ATRN) for learning and recognition of temporal correlations of temporal patterns. The ATRN employs adaptive time-delays and recurrent connections, which are inspired from neurobiology. In the ATRN, the adaptive time-delays make the ATRN choose the optimal values of time-delays for the temporal location of the important information in the input parrerns, and the recurrent connections enable the network to encode and integrate temporal information of sequences which have arbitrary interval time and arbitrary length of temporal context. The ATRN described in this paper, ATNN proposed by Lin, and TDNN introduced by Waibel were simulated and applied to the chaotic time series preditcion of Mackey-Glass delay-differential equation. The simulation results show that the normalized mean square error (NMSE) of ATRN is 0.0026, while the NMSE values of ATNN and TDNN are 0.014, 0.0117, respectively, and in temporal learning, employing recurrent links in the network is more effective than putting multiple time-delays into the neurons. The best performance is attained bythe ATRN. This ATRN will be sell applicable for temporally continuous domains, such as speech recognition, moving object recognition, motor control, and time-series prediction.

• Childhood Kidney Diseases
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• v.28 no.1
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• pp.35-43
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• 2024

Purpose: Recurrent urinary tract infections (UTIs) in children is a major challenge for pediatricians. This study was designed to investigate the risk factors for recurrent UTIs and determine the association between recurrent UTIs and clinical findings, including growth patterns in infants and children younger than 24 months of age. Methods: We retrospectively reviewed the medical records of 147 patients <24 months of age with UTIs who were hospitalized between August 2018 and October 2021. The patients were divided into recurrent and single UTI episode groups. Clinical findings and anthropometric and laboratory data were compared between the two groups. Results: In the recurrent UTI group, the weight-for-length (WFL) percentile at the first UTI diagnosis was lower compared to the single UTI episode group, and the weight-for-age percentile at 3-month and 6-month follow-ups after the first UTI decreased (all P<0.05). In univariable logistic regression analysis, higher birth weight, lower WFL percentile, the presence of hydronephrosis, acute pyelonephritis or vesicoureteral reflux, the use of prophylactic antibiotics, and non-Escherichia coli infections were associated with the development of recurrent UTIs (all P<0.05). However, in the multivariable analysis, only the presence of hydronephrosis and prophylactic antibiotic use were independently related to UTI recurrence (P<0.05). Conclusions: The presence of hydronephrosis at the first UTI can be helpful for predicting UTI recurrence in young children aged <24 months. Antibiotic prophylaxis may be associated with UTI recurrence. Potential growth delay should be carefully monitored in infants with recurrent UTI.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• Journal of Gastric Cancer
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• v.22 no.4
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• pp.369-380
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• 2022

Purpose: Tumor recurrence is the principal cause of poor outcomes in remnant gastric cancer (RGC) after resection. We sought to elucidate the recurrent patterns according to tumor locations in RGC. Materials and Methods: Data were collected from the Shanghai Cancer Center between January 2006 and December 2020. A total of 129 patients with RGC were included in this study, of whom 62 had carcinomas at the anastomotic site (group A) and 67 at the non-anastomotic site (group N). The clinicopathological characteristics, surgical results, recurrent diseases, and survival were investigated according to tumor location. Results: The time interval from the previous gastrectomy to the current diagnosis was 32.0±13.0 and 21.0±13.4 years in groups A and N, respectively. The previous disease was benign in 51/62 cases (82.3%) in group A and 37/67 cases (55.2%) in group N (P=0.002). Thirty-three patients had documented sites of tumor recurrence through imaging or pathological examinations. The median time to recurrence was 11.0 months (range, 1.0-35.1 months). Peritoneal recurrence occurred in 11.3% (7/62) of the patients in group A versus 1.5% (1/67) of the patients in group N (P=0.006). Hepatic recurrence occurred in 3.2% (2/62) of the patients in group A versus 13.4% (9/67) of the patients in group N (P=0.038). Patients in group A had significantly better overall survival than those in group N (P=0.046). Conclusions: The tumor location of RGC is an essential factor for predicting recurrence patterns and overall survival. When selecting an optimal postoperative follow-up program for RGC, physicians should consider recurrent features according to the tumor location.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.1-6
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• 2024

Recently, artificial neural networks have been playing a crucial role and advancing across various fields. Artificial neural networks are typically categorized into feedforward neural networks and recurrent neural networks. However, feedforward neural networks are primarily used for processing static spatial patterns such as image recognition and object detection. They are not suitable for handling temporal signals. Recurrent neural networks, on the other hand, face the challenges of complex training procedures and requiring significant computational power. In this paper, we propose memristors suitable for an advanced form of recurrent neural networks called reservoir computing systems, utilizing a mask processor. Using the characteristic equations of Ti/TiOx/TaOy/Pt, Pt/TiOx/Pt, and Ag/ZnO-NW/Pt memristors, we generated current-voltage curves to verify their memristive behavior through the confirmation of hysteresis. Subsequently, we trained and inferred reservoir computing systems using these memristors with the NIST TI-46 database. Among these systems, the accuracy of the reservoir computing system based on Ti/TiOx/TaOy/Pt memristors reached 99%, confirming the Ti/TiOx/TaOy/Pt memristor structure's suitability for inferring speech recognition tasks.

• Radiation Oncology Journal
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• v.37 no.3
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• pp.193-200
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• 2019

Purpose: To explore the role of salvage radiotherapy (RT) for recurrent thymoma as an alternative to surgery. Materials and Methods: Between 2007 and 2015, 47 patients who received salvage RT for recurrent thymoma at Yonsei Cancer Center were included in this study. Recurrent sites included initial tumor bed (n = 4), pleura (n = 19), lung parenchyma (n = 10), distant (n = 9), and multiple regions (n = 5). Three-dimensional conformal and intensity-modulated RT were used in 29 and 18 patients, respectively. Median prescribed dose to gross tumor was 52 Gy (range, 30 to 70 Gy), with equivalent doses in 2-Gy fractions (EQD₂). We investigated overall survival (OS), progression-free survival (PFS), and patterns of failure. Local failure after salvage RT was defined as recurrence at the target volume receiving >50% of the prescription dose. Results: Median follow-up time was 83 months (range, 8 to 299 months). Five-year OS and PFS were 70% and 22%, respectively. The overall response rate was 97.9%; complete response, 34%; partial response, 44.7%; and stable disease, 19.1%. In multivariate analysis, histologic type and salvage RT dose (≥52 Gy, EQD₂) were significantly associated with OS. The high dose group (≥52 Gy, EQD₂) had significantly better outcomes than the low dose group (5-year OS: 80% vs. 59%, p = 0.046; 5-year PFS: 30% vs. 14%, p=0.002). Treatment failure occurred in 34 patients; out-of-field failure was dominant (intra-thoracic recurrence 35.3%; extrathoracic recurrence 11.8%), while local failure rate was 5.8%. Conclusion: Salvage RT for recurrent thymoma using high doses and advanced precision techniques produced favorable outcomes, providing evidence that recurrent thymoma is radiosensitive.