An Implementation of Web-based Machining Operation Planning

AbstractThis paper introduces a machining operation planning system for practical use in small and medium sized manufacturers. The system passes through two stages: manual input of required information and semi-automated generation of an operation plan. At the first stage, removal volumes out of a workpiece and their reference details on the part drawing are translated into the specific machining features manually. The operations to be performed for each feature are then selected from the operation list related to the feature. At the second stage, the cutting tool for each operation is determined with its proper cutting conditions by using a rule-based algorithm and retrieving a machining database gradually updated. The web interface makes it possible for the manufacturers to keep a record of their machining practice in the database and obtain the favorable data from the various sources when needed. An example is shown to demonstrate the usefulness of the system

Measurement of craving among gamers with internet gaming disorder using repeated presentations of game videos: a resting-state electroencephalography study

Abstract Background Internet gaming disorder (IGD) is receiving increasing attention owing to its effects on daily living and psychological function. Methods In this study, electroencephalography was used to compare neural activity triggered by repeated presentation of a stimulus in healthy controls (HCs) and those with IGD. A total of 42 adult men were categorized into two groups (IGD, n = 21) based on Y-IAT-K scores. Participants were required to watch repeated presentations of video games while wearing a head-mounted display, and the delta (D), theta (T), alpha (A), beta (B), and gamma (G) activities in the prefrontal (PF), central (C), and parieto-occipital (PO) regions were analyzed. Results The IGD group exhibited higher absolute powers of DC, DPO, TC, TPO, BC, and BPO than HCs. Among the IGD classification models, a neural network achieves the highest average accuracy of 93% (5-fold cross validation) and 84% (test). Conclusions These findings may significantly contribute to a more comprehensive understanding of the neurological features associated with IGD and provide potential neurological markers that can be used to distinguish between individuals with IGD and HCs

DEVELOPING AN ENHANCED PORTFOLIO TRADING SYSTEM USING K-MEANS AND GENETIC ALGORITHMS

The objective of this study is to enhance the ability of an index fund strategy using k-means clustering and genetic algorithms. This study proposes a novel enhanced portfolio mechanism consisting of two phases. In the first phase, a subset of all the index shares is selected using k-means clustering based on investor information. In the second phase, a genetic algorithm is employed to search for the optimal stock weights in the selected clusters. In order to identify the usefulness of the proposed model, this study is compared with the conventional approach. For measuring trading performance, the tracking error, which a measure of how closely a portfolio follows the index as a benchmark, is evaluated. Furthermore, the information ratio is used to compare the performance of the proposed model in terms of the risk-adjusted return. An empirical study of the proposed model is simulated in the Korea stock exchange market

An Intelligent Hybrid Trading System for Discovering Trading Rules for the Futures Market using Rough Sets and Genetic Algorithms

Discovering intelligent technical trading rules from nonlinear and complex stock market data, and then developing decision support trading systems, is an important challenge. The objective of this study is to develop an intelligent hybrid trading system for discovering technical trading rules using rough set analysis and a genetic algorithm (GA). In order to obtain better trading decisions, a novel rule discovery mechanism using a GA approach is proposed for solving optimization problems (i.e., data discretization and reducts) of rough set analysis when discovering technical trading rules for the futures market. Experiments are designed to test the proposed model against comparable approaches (i.e., random, correlation, and GA approaches). In addition, these comprehensive experiments cover most of the current trading system topics, including the use of a sliding window method (with or without validation dataset), the number of trading rules, and the size of training period. To evaluate an intelligent hybrid trading system, experiments were carried out on the historical data of the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. In particular, trading performance is analyzed according to the number of sets of decision rules and the size of the training period for discovering trading rules for the testing period. The results show that the proposed model significantly outperforms the benchmark model in terms of the average return and as a risk-adjusted measure

Early Prediction of Sepsis Onset Using Neural Architecture Search Based on Genetic Algorithms

Sepsis is a life-threatening condition with a high mortality rate. Early prediction and treatment are the most effective strategies for increasing survival rates. This paper proposes a neural architecture search (NAS) model to predict the onset of sepsis with a low computational cost and high search performance by applying a genetic algorithm (GA). The proposed model shares the weights of all possible connection nodes internally within the neural network. Externally, the search cost is reduced through the weight-sharing effect between the genotypes of the GA. A predictive analysis was performed using the Medical Information Mart for Intensive Care III (MIMIC-III), a medical time-series dataset, with the primary objective of predicting sepsis onset 3 h before occurrence. In addition, experiments were conducted under various prediction times (0–12 h) for comparison. The proposed model exhibited an area under the receiver operating characteristic curve (AUROC) score of 0.94 (95% CI: 0.92–0.96) for 3 h, which is 0.31–0.26 higher than the scores obtained using the Sequential Organ Failure Assessment (SOFA), quick SOFA (qSOFA), and Simplified Acute Physiology Score (SAPS) II scoring systems. Furthermore, the proposed model exhibited a 12% improvement in the AUROC value over a simple model based on the long short-term memory neural network. Additionally, it is not only optimally searchable for sepsis onset prediction, but also outperforms conventional models that use similar predictive purposes and datasets. Notably, it is sufficiently robust to shape changes in the input data and has less structural dependence

Free-Space Permittivity Measurement for Inhomogeneous PSV-Coated Si-wafer at Frequencies from 75 GHz to 325 GHz

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3D bioprinting of a trachea-mimetic cellular construct of a clinically relevant size

Despite notable advances in extrusion-based 3D bioprinting, it remains a challenge to create a clinically-sized cellular construct using extrusion-based 3D printing due to long printing times adversely affecting cell viability and functionality. Here, we present an advanced extrusion-based 3D bioprinting strategy composed of a two-step printing process to facilitate creation of a trachea-mimetic cellular construct of clinically relevant size. A porous bellows framework is first printed using typical extrusion-based 3D printing. Selective printing of cellular components, such as cartilage rings and epithelium lining, is then performed on the outer grooves and inner surface of the bellows framework by a rotational printing process. With this strategy, 3D bioprinting of a tracheamimetic cellular construct of clinically relevant size is achieved in significantly less total printing time compared to a typical extrusion-based 3D bioprinting strategy which requires printing of an additional sacrificial material. Tracheal cartilage formation was successfully demonstrated in a nude mouse model through a subcutaneous implantation study of trachea-mimetic cellular constructs wrapped with a sinusoidal-patterned tubular mesh preventing rapid resorption of cartilage rings in vivo. This two-step 3D bioprinting for a trachea-mimetic cellular construct of clinically relevant size can provide a fundamental step towards clinical translation of 3D bioprinting based tracheal reconstruction.11Nsciescopu