Application of Recent Developments in Deep Learning to ANN-based Automatic Berthing Systems

Previous studies on Artificial Neural Network (ANN)-based automatic berthing showed considerable increases in performance by training ANNs with a set of berthing datasets. However, the berthing performance deteriorated when an extrapolated initial position was given. To overcome the extrapolation problem and improve the training performance, recent developments in Deep Learning (DL) are adopted in this paper. Recent activation functions, weight initialization methods, input data-scaling methods, a higher number of hidden layers, and Batch Normalization (BN) are considered, and their effectiveness has been analyzed based on loss functions, berthing performance histories, and berthing trajectories. Finally, it is shown that the use of recent activation and weight initialization method results in faster training convergence and a higher number of hidden layers. This leads to a better berthing performance over the training dataset. It is found that application of the BN can overcome the extrapolated initial position problem

Organizing Pneumonia by Paragonimiasis and Coexistent Aspergilloma Manifested as a Pulmonary Irregular Nodule

Organizing pneumonia by paragonimiasis and coexistent aspergilloma as a pulmonary nodule is a rare case of lung disease. Its radiographic or CT feature has not been described before in the radiologic literature. We present organizing pneumonia by paragonimiasis and coexistent aspergilloma manifested as a pulmonary irregular nodule on CT

The Fit between Client IT Capability and Vendor Competence and Its Impact on Outsourcing Success

This study investigates the impact of client firm’s IT capability, vendor firm’s competence and their fit on the outsourcing success. In theory building, by concretizing the concepts of IT capability and competence based on the resource-based view, the importance of fit between the client’s IT capability and the vendor’s competence is emphasized. We then hypothesize that both factors are stronger together than the individual impact of either the client’s IT capability or the vendor’s competence. For validation, 267 client-vendor-matched-pair data were collected. To avoid potential imbalance caused by the bilateral perspective, an exploratory approach, all-possible-subsets-regression method was adopted. The results reveal that the vendor’s competence is the most significant factor in outsourcing success, but interestingly, the fit between vendor competence and the client’s IT capability is the second most important. The client’s IT capability also has a positive impact on outsourcing success but with the smallest explanation power

Socio-economic factors associated with mental health outcomes during the COVID-19 pandemic in South Korea

BackgroundIndividuals are at an increased risk of adverse mental health outcomes during the COVID-19 pandemic. To reduce the impact on mental health outcomes that were induced by national-level policies, which may influence an individual at the community level, exploring the comprehensive relations between individual and environmental factors are needed. The aim is to examine socio-ecological factors associated with mental health outcomes, including depressive and anxiety symptoms, with the perspective of support to provide interventions that help the community during future disease outbreaks.MethodFrom 5 November to 20 November 2020, a cross-sectional and population-based study was conducted to assess the socio-ecological factors of mental health outcomes during the COVID-19 pandemic. A total of 1,000 participants, aged 20–69 years, in Chungnam Region, South Korea, were included in this study. Multiple linear regression models were used to examine the association between socio-ecological factors and mental health outcomes. The primary outcomes were individuals' mental health outcomes which are measured by PHQ-9 and GAD-7 scores.ResultsOf the 1,000 participants, the average PHQ-9 was 4.39, and GAD-7 was 3.21 during the COVID-19 pandemic. Specifically, the participants with moderate or severe levels of PHQ-9 and GAD-7 were 12.6 and 6.8%, respectively. Higher levels of depressive and anxiety symptoms were associated with participants who were single, reported a lower household income, had decreased support from friends or family, and increased stress from the workplace or home. In subgroup analyses by age, gender, and household income, a similar trend was reported in individual and interpersonal-level factors. There were significant associations between regional-level factors, including gross regional domestic product (GRDP), mental health institutions, psychiatrists, nurse-to-population ratios, and individuals' mental health outcomes.ConclusionThe management of depressive and anxiety symptoms of individuals during the pandemic was better explained by individual and interpersonal characteristics rather than regional-level factors, highlighting the need for more policies aimed at these lower levels

Effect of data normalization on fuzzy clustering of DNA microarray data

BACKGROUND: Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. Gene expression data is information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. Clustering is an important tool for finding groups of genes with similar expression patterns in microarray data analysis. However, hard clustering methods, which assign each gene exactly to one cluster, are poorly suited to the analysis of microarray datasets because in such datasets the clusters of genes frequently overlap. RESULTS: In this study we applied the fuzzy partitional clustering method known as Fuzzy C-Means (FCM) to overcome the limitations of hard clustering. To identify the effect of data normalization, we used three normalization methods, the two common scale and location transformations and Lowess normalization methods, to normalize three microarray datasets and three simulated datasets. First we determined the optimal parameters for FCM clustering. We found that the optimal fuzzification parameter in the FCM analysis of a microarray dataset depended on the normalization method applied to the dataset during preprocessing. We additionally evaluated the effect of normalization of noisy datasets on the results obtained when hard clustering or FCM clustering was applied to those datasets. The effects of normalization were evaluated using both simulated datasets and microarray datasets. A comparative analysis showed that the clustering results depended on the normalization method used and the noisiness of the data. In particular, the selection of the fuzzification parameter value for the FCM method was sensitive to the normalization method used for datasets with large variations across samples. CONCLUSION: Lowess normalization is more robust for clustering of genes from general microarray data than the two common scale and location adjustment methods when samples have varying expression patterns or are noisy. In particular, the FCM method slightly outperformed the hard clustering methods when the expression patterns of genes overlapped and was advantageous in finding co-regulated genes. Thus, the FCM approach offers a convenient method for finding subsets of genes that are strongly associated to a given cluster

Feature Selection for Very Short-Term Heavy Rainfall Prediction Using Evolutionary Computation

We developed a method to predict heavy rainfall in South Korea with a lead time of one to six hours. We modified the AWS data for the recent four years to perform efficient prediction, through normalizing them to numeric values between 0 and 1 and undersampling them by adjusting the sampling sizes of no-heavy-rain to be equal to the size of heavy-rain. Evolutionary algorithms were used to select important features. Discriminant functions, such as support vector machine (SVM), k-nearest neighbors algorithm (k-NN), and variant k-NN (k-VNN), were adopted in discriminant analysis. We divided our modified AWS data into three parts: the training set, ranging from 2007 to 2008, the validation set, 2009, and the test set, 2010. The validation set was used to select an important subset from input features. The main features selected were precipitation sensing and accumulated precipitation for 24 hours. In comparative SVM tests using evolutionary algorithms, the results showed that genetic algorithm was considerably superior to differential evolution. The equitable treatment score of SVM with polynomial kernel was the highest among our experiments on average. k-VNN outperformed k-NN, but it was dominated by SVM with polynomial kernel