• Horticultural Science & Technology
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• v.34 no.1
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• pp.94-101
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• 2016

The purpose of this study was to establish postharvest management techniques including a temperature pre-conditioning protocol for maintaining fruit quality in newly developed early-season Korean pear cultivar 'Hanareum' (Pyrus pyrifolia Nakai). The fruits were treated with three different pre-conditioning temperatures (21, 25, or $29^{\circ}C$) for 4 days according to the harvest time (103 or 110 days after full bloom, DAFB). The percent weight loss was relatively low in the fruits subjected to low pre-conditioning temperature regardless of harvest time. The firmness of the fruits treated with pre-conditioning at $21^{\circ}C$ remained high during 20 days of simulated marketing at $25^{\circ}C$, although all treated fruits showed a general decline of firmness with extended time of simulated marketing. These fruits also showed higher appearance and a lower incidence of mealiness disorder symptoms. During the experimental periods, the production of ethylene was lower in the fruits pre-conditioned at $21^{\circ}C$ in comparison with those of treated at 25 and $29^{\circ}C$. High respiration rates were obvious in the fruits pre-conditioned at high temperature ($29^{\circ}C$), especially in the optimum-harvested fruits, where respiration was approximately two times higher than that of fruits exposed to $21^{\circ}C$ during pre-conditioning. However, the respiration rate was similar during simulated marketing at $25^{\circ}C$ regardless of harvest time. These results demonstrated that temperature pre-conditioning at $21^{\circ}C$ is a simple and effective postharvest technique for summer harvested Korean pear cultivars including 'Hanareum'.

• Journal of Dental Rehabilitation and Applied Science
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• v.35 no.3
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• pp.160-169
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• 2019

Purpose: The purpose of this study was to evaluate the antimicrobial effects of a toothbrush with light-emitting diodes (LEDs) on periodontitis-associated dental biofilm attached to a zirconia surface by static and dynamic methods. Materials and Methods: Zirconia disks (12 mm diameter, 2.5 mm thickness) were inserted into a 24-well plate (static method) or inside a Center for Disease Control and Prevention (CDC) biofilm reactor (dynamic method) to form dental biofilms using Streptococcus gordonii and Fusobacterium nucleatum. The disks with biofilm were subdivided into five treatment groups-control, commercial photodynamic therapy (PDT), toothbrush alone (B), brush with LED (BL), and brush with LED+erythrosine (BLE). After treatment, the disks were agitated to detach the bacteria, and the resulting solutions were spread directly on selective agar. The number of viable bacteria and percentage of bacterial reduction were determined from colony counts. Scanning electron microscopy (SEM) was performed to visualize alterations in bacterial morphology. Results: No significant difference in biofilm formation was observed between dynamic and static methods. A significant difference was observed in the number of viable bacteria between the control and all experimental groups (P < 0.05). The percentage of bacterial reduction in the BLE group was significantly higher than in the other treated groups (P < 0.05). SEM revealed damaged bacterial cell walls in the PDT, BL, and BLE groups, but intact cell walls in the control and B groups. Conclusion: The findings suggest that an LED toothbrush with erythrosine is more effective than other treatments in reducing the viability of periodontitis-associated bacteria attached to zirconia in vitro.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.