• Journal of Gastric Cancer
• /
• v.6 no.3
• /
• pp.167-172
• /
• 2006

Purpose: The laparoscopy assisted gastrectomy has been increasingly reported as the treatment of choice for early gastric cancer. However, expert surgeons, who have performed a conventional open gastrectomy for a long time, tend to have a negative attitude toward laparoscopic procedures. The aim of this study was to determine the learning curve of a laparoscopy assisted distal gastrectomy (LADG) for a surgeon expert in performing an open gastrectomy and to analyze the factors that have an effect on a LADG. Materials and Methods: Between April 2005 and March 2006, 62 patients underwent a LADG with D1+beta lymph-node dissection. The 62 patients were divided into 10 sequential groups with 6 cases in each group (the last group was 8 cases), and the time required to reach the plateau of the learning curve was determined by examining the average operative times of these 10 groups. Other factors, such as sex, BMI, complications, transfusion requirements, the number of retrieved lymph nodes, and change of postoperative hemoglobin level, were also analyzed. Results: With the $5^{th}$ group (after 30 cases), the operative time reached a plateau (average: 170 min/operation). The differences between before the $30^{th}$ case and after the $31^{st}$ case with respect to changes in the postoperative hemoglobin level, the number of retrieved lymph nodes, the transfusion requirements, and the complications rate were not significant. Conclusion: According to an analysis of the operative time, experience with 30 LADGs in patients with early gastric cancer is the point at which the plateau of the learning curve (7 months) is reached. Abundant experience with a conventional open gastrectomy and a well-organized laparoscopic surgery team are important factors in overcoming the learning curie earlier.

• Journal of the Ergonomics Society of Korea
• /
• v.11 no.2
• /
• pp.47-56
• /
• 1992

In this paper, a mathematical model of learning curve is proposed to study the fi- nger's reaction time. The model is a logarithmic linear type which represents a lear- ning curve appropriately, and parameters are estimated by the linear. The learning coefficient and percentage of a reaction time can easily computed in the mathematical model. This quantitative approach provieds an important information to be used fot the working capqbility qualification of re-employment as well as the adaptability estimation of aged workers.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.13
• /
• pp.5395-5399
• /
• 2014

Purpose: This study was designed to evaluate the outcomes of laparoscopic colorectal resection in a period of learning curve completed by surgeons with different experience and aptitudes with a view to making clear whether seniors had a better learning curve compared with juniors. Methods: From May 2010 to August 2012, the first twenty patients underwent laparoscopic colorectal resection completed by each surgeon were selected for analysis retrospectively. A total of 240 patients treated by 5 seniors and 7 juniors were divided into the senior group (n=100) and the junior group (n=140). The short-term outcomes of laparoscopic surgery of the two groups were compared. Results: The mean numbers of lymph nodes harvested were $21.2{\pm}11.0$ in the senior group and $17.3{\pm}11.5$ in the junior group (p=0.010); The mean operative times were $187.9{\pm}60.0min$ as compared to $231.3{\pm}55.7min$ (p=0.006), and blood loss values were $177.0{\pm}100.7ml$ and $234.0{\pm}185ml$, respectively (p=0.001); Conversion rate in the senior group was obviously lower than in the junior group (10.0% vs 20.7%, p=0.027) and the mean time to passing of first flatus were $3.3{\pm}0.9$ and $3.8{\pm}0.9$ days (p=0.001). For low rectal cancer, the sphincter preserving rates were 68.7% and 35.3% (p=0.027). Conclusions: Seniors could perform laparoscopic colorectal resection with relatively better oncological outcomes and quicker recovery, and seniors could master the laparoscopic skill more easily and quickly. Seniors had a better learning curve for laparoscopic colorectal cancer resection compared to juniors.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39B no.8
• /
• pp.518-527
• /
• 2014

This study is to estimate the learning curve based on the consequences of reduced voice of customer from each telecommunication service products. We used Exponential Decay Model, which is the most popular among the learning curve models. We attempted to add how VOC changes in accordance with seasonal factors, human resource input, application of software, and the investment. The results of the empirical analysis of each service product as follows: First, as learning curve, customer complaints decreased. Second, human resource input, Network fault make increase or decrease customer complaints(VOC). Third, even though increasing the customer's quality of experience, VOC would not decrease due to service paradox.

• Journal of Energy Engineering
• /
• v.15 no.4 s.48
• /
• pp.209-228
• /
• 2006

The learning effect is one of the theoretical frameworks that examine the mechanisms of the deployment of energy technologies. The objective of this paper is to provide a theoretical overview and a critical analysis of the literature on the experience curve for energy technology. For these objectives, we review a couple of theoretical aspects and applications and investigate the sources of learning and cost reductions to grasp the mechanisms of teaming effect. Finally we conclude some insights from our theoretical reviews.

• International journal of advanced smart convergence
• /
• v.10 no.3
• /
• pp.51-58
• /
• 2021

In this paper, we propose a deep learning classifier for estimating the number of layers in the Earth's structure. When installing a grounding system, knowledge of the subsurface in the area is absolutely necessary. The subsurface structure can be modeled by the earth parameters. Knowing the exact number of layers can significantly reduce the amount of computation to estimate these parameters. The classifier consists of a feedforward neural network. Apparent resistivity curves were used to train the deep learning classifier. The apparent resistivity at 20 equally spaced log points in each curve are used as the features for the input of the deep learning classifier. Apparent resistivity curve data sets are collected either by theoretical calculations or by Wenner's measurement method. Deep learning classifiers are coded by Keras, an open source neural network library written in Python. This model has been shown to converge with close to 100% accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.10
• /
• pp.5159-5178
• /
• 2018

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.3
• /
• pp.263-267
• /
• 2005

Building classifiers for financial real-world classification problems is often plagued by severely overlapping and highly skewed class distribution. New performance measures such as receiver operating characteristic (ROC) curve and area under ROC curve (AUC) have been recently introduced in evaluating and building classifiers for those kind of problems. They are, however, in-effective to evaluation of classifier's discrimination performance in a particular class of the classification problems that interests lie in only a local operating range of the classifier, In this paper, a new method is proposed that enables us to directly improve classifier's discrimination performance at a desired local operating range by defining and optimizing a partial area under ROC curve or domain-specific curve, which is difficult to achieve with conventional classification accuracy based learning methods. The effectiveness of the proposed approach is demonstrated in terms of fraud detection capability in a real-world fraud detection problem compared with the MSE-based approach.

• Journal of Gastric Cancer
• /
• v.18 no.1
• /
• pp.90-98
• /
• 2018

Purpose: We evaluated the learning curve and short-term surgical outcomes of robot-assisted distal gastrectomy (RADG) performed by a single surgeon experienced in open, but not laparoscopic, gastrectomy. We aimed to verify the feasibility of performing RADG without extensive laparoscopic experience. Materials and Methods: Between July 2012 and December 2016, 60 RADG procedures were performed by a single surgeon using the da $Vinci^{(R)}$ Surgical System (Intuitive Surgical). Patient characteristics, the length of the learning curve, surgical parameters, and short-term postoperative outcomes were analyzed and compared before and after the learning curve had been overcome. Results: The duration of surgery rapidly decreased from the first to the fourth case; after 25 procedures, the duration of surgery was stabilized, suggesting that the learning curve had been overcome. Cases were divided into 2 groups: 25 cases before the learning curve had been overcome (early cases) and 35 later cases. The mean duration of surgery was 420.8 minutes for the initial cases and 281.7 minutes for the later cases (P<0.001). The console time was significantly shorter during the later cases (168.6 minutes) than during the early cases (247.1 minutes) (P<0.001). Although the volume of blood loss during surgery declined over time, there was no significant difference between the early and later cases. No other postoperative outcomes differed between the 2 groups. Pathology reports revealed the presence of mucosal invasion in 58 patients and submucosal invasion in 2 patients. Conclusions: RADG can be performed safely with acceptable surgical outcomes by experts in open gastrectomy.

• Journal of Korean Neurosurgical Society
• /
• v.67 no.1
• /
• pp.60-72
• /
• 2024

Objective : Recently, robotic-assisted spine surgery (RASS) has been considered a minimally invasive and relatively accurate method. In total, 495 robotic-assisted pedicle screw fixation (RAPSF) procedures were attempted on 100 patients during a 14-month period. The current study aimed to analyze the accuracy, potential risk factors, and learning curve of RAPSF. Methods : This retrospective study evaluated the position of RAPSF using the Gertzbein and Robbins scale (GRS). The accuracy was analyzed using the ratio of the clinically acceptable group (GRS grades A and B), the dissatisfying group (GRS grades C, D, and E), and the Surgical Evaluation Assistant program. The RAPSF was divided into the no-breached group (GRS grade A) and breached group (GRS grades B, C, D, and E), and the potential risk factors of RAPSF were evaluated. The learning curve was analyzed by changes in robot-used time per screw and the occurrence tendency of breached and failed screws according to case accumulation. Results : The clinically acceptable group in RAPSF was 98.12%. In the analysis using the Surgical Evaluation Assistant program, the tip offset was 2.37±1.89 mm, the tail offset was 3.09±1.90 mm, and the angular offset was 3.72°±2.72°. In the analysis of potential risk factors, the difference in screw fixation level (p=0.009) and segmental distance between the tracker and the instrumented level (p=0.001) between the no-breached and breached group were statistically significant, but not for the other factors. The mean difference between the no-breach and breach groups was statistically significant in terms of pedicle width (p<0.001) and tail offset (p=0.042). In the learning curve analysis, the occurrence of breached and failed screws and the robot-used time per screw screws showed a significant decreasing trend. Conclusion : In the current study, RAPSF was highly accurate and the specific potential risk factors were not identified. However, pedicle width was presumed to be related to breached screw. Meanwhile, the robot-used time per screw and the incidence of breached and failed screws decreased with the learning curve.