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Recent improvements in the performance of the human face recognition model have led to the development of relevant products and services. However, research in the similar field of animal face identification has remained relatively limited due to the greater diversity and complexity in shape and the lack of relevant data for animal faces such as dogs. In the face identification model using triplet loss, the length of the embedding vector is normalized by adding an L2-normalization (L2-norm) layer for using cosine-similarity-based learning. As a result, object identification depends only on the angle, and the distribution of the embedding vector is limited to the surface of a sphere with a radius of 1. This study proposes training the model from which the L2-norm layer is removed by using the triplet loss to utilize a wide vector space beyond the surface of a sphere with a radius of 1, for which a novel loss function and its two-stage learning method. The proposed method classifies the embedding vector within a space rather than on the surface, and the model’s performance is also increased. The accuracy, one-shot identification performance, and distribution of the embedding vectors are compared between the existing learning method and the proposed learning method for verification. The verification was conducted using an open-set. The resulting accuracy of 97.33% for the proposed learning method is approximately 4% greater than that of the existing learning method.
Bohan Yoon; Hyeonji So; Jongtae Rhee. A Methodology for Utilizing Vector Space to Improve the Performance of a Dog Face Identification Model. Applied Sciences 2021, 11, 2074 .
AMA StyleBohan Yoon, Hyeonji So, Jongtae Rhee. A Methodology for Utilizing Vector Space to Improve the Performance of a Dog Face Identification Model. Applied Sciences. 2021; 11 (5):2074.
Chicago/Turabian StyleBohan Yoon; Hyeonji So; Jongtae Rhee. 2021. "A Methodology for Utilizing Vector Space to Improve the Performance of a Dog Face Identification Model." Applied Sciences 11, no. 5: 2074.
Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.
Ganjar Alfian; Muhammad Syafrudin; Norma Latif Fitriyani; Muhammad Anshari; Pavel Stasa; Jiri Svub; Jongtae Rhee. Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors. Mathematics 2020, 8, 1620 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Norma Latif Fitriyani, Muhammad Anshari, Pavel Stasa, Jiri Svub, Jongtae Rhee. Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors. Mathematics. 2020; 8 (9):1620.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Norma Latif Fitriyani; Muhammad Anshari; Pavel Stasa; Jiri Svub; Jongtae Rhee. 2020. "Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors." Mathematics 8, no. 9: 1620.
Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving the model’s performance. To validate the effectiveness of GA-XGBoost, we present six experiments: comparing GA-XGBoost with other machine learning models and previous study results, a statistical significant test, impact analysis of feature selection and comparison with other feature selection methods, and sensitivity analysis of GA parameters. During the experiments, we use accuracy, precision, recall, and f1-score to measure the performance of the prediction models. The results show that GA-XGBoost obtains better performance than other prediction models and the previous study results. In addition, we design and develop a web-based self-care prediction to help therapist diagnose the self-care problems of children with disabilities. Therefore, appropriate treatment/therapy could be performed for each child to improve their therapeutic outcome.
Muhammad Syafrudin; Ganjar Alfian; Norma Latif Fitriyani; Muhammad Anshari; Tony Hadibarata; Agung Fatwanto; Jongtae Rhee. A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting. Mathematics 2020, 8, 1590 .
AMA StyleMuhammad Syafrudin, Ganjar Alfian, Norma Latif Fitriyani, Muhammad Anshari, Tony Hadibarata, Agung Fatwanto, Jongtae Rhee. A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting. Mathematics. 2020; 8 (9):1590.
Chicago/Turabian StyleMuhammad Syafrudin; Ganjar Alfian; Norma Latif Fitriyani; Muhammad Anshari; Tony Hadibarata; Agung Fatwanto; Jongtae Rhee. 2020. "A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting." Mathematics 8, no. 9: 1590.
Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.
Ganjar Alfian; Muhammad Syafrudin; Bohan Yoon; Jongtae Rhee. False Positive RFID Detection Using Classification Models. Applied Sciences 2019, 9, 1154 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Bohan Yoon, Jongtae Rhee. False Positive RFID Detection Using Classification Models. Applied Sciences. 2019; 9 (6):1154.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Bohan Yoon; Jongtae Rhee. 2019. "False Positive RFID Detection Using Classification Models." Applied Sciences 9, no. 6: 1154.
Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model obtains environmental data from various sensors including gyroscopes, accelerometers, temperature, humidity, ambient light, and air quality. The fault model is installed close to the facilities, so abnormal events can be timely detected. Several performance evaluations are conducted to obtain the optimal scenario for utilizing edge devices to improve data processing and analysis speed, and the final proposed model provides the highest accuracy in terms of detecting abnormal events compared to other classification models. The proposed model was tested over four months of operation in a Korean automobile parts factory, and provided significant benefits from monitoring assembly line, as well as classifying abnormal events. The model helped improve decision-making by reducing or preventing unexpected losses due to abnormal events.
Muhammad Syafrudin; Norma Latif Fitriyani; Ganjar Alfian; Jongtae Rhee. An Affordable Fast Early Warning System for Edge Computing in Assembly Line. Applied Sciences 2018, 9, 84 .
AMA StyleMuhammad Syafrudin, Norma Latif Fitriyani, Ganjar Alfian, Jongtae Rhee. An Affordable Fast Early Warning System for Edge Computing in Assembly Line. Applied Sciences. 2018; 9 (1):84.
Chicago/Turabian StyleMuhammad Syafrudin; Norma Latif Fitriyani; Ganjar Alfian; Jongtae Rhee. 2018. "An Affordable Fast Early Warning System for Edge Computing in Assembly Line." Applied Sciences 9, no. 1: 84.
With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.
Muhammad Syafrudin; Ganjar Alfian; Norma Latif Fitriyani; Jongtae Rhee. Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing. Sensors 2018, 18, 2946 .
AMA StyleMuhammad Syafrudin, Ganjar Alfian, Norma Latif Fitriyani, Jongtae Rhee. Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing. Sensors. 2018; 18 (9):2946.
Chicago/Turabian StyleMuhammad Syafrudin; Ganjar Alfian; Norma Latif Fitriyani; Jongtae Rhee. 2018. "Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing." Sensors 18, no. 9: 2946.
As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.
Muhammad Fazal Ijaz; Ganjar Alfian; Muhammad Syafrudin; Jongtae Rhee. Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest. Applied Sciences 2018, 8, 1325 .
AMA StyleMuhammad Fazal Ijaz, Ganjar Alfian, Muhammad Syafrudin, Jongtae Rhee. Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest. Applied Sciences. 2018; 8 (8):1325.
Chicago/Turabian StyleMuhammad Fazal Ijaz; Ganjar Alfian; Muhammad Syafrudin; Jongtae Rhee. 2018. "Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest." Applied Sciences 8, no. 8: 1325.
Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning–based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.
Ganjar Alfian; Muhammad Syafrudin; Muhammad Fazal Ijaz; M. Alex Syaekhoni; Norma Latif Fitriyani; Jongtae Rhee. A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing. Sensors 2018, 18, 2183 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Muhammad Fazal Ijaz, M. Alex Syaekhoni, Norma Latif Fitriyani, Jongtae Rhee. A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing. Sensors. 2018; 18 (7):2183.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Muhammad Fazal Ijaz; M. Alex Syaekhoni; Norma Latif Fitriyani; Jongtae Rhee. 2018. "A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing." Sensors 18, no. 7: 2183.
Muhammad Syafrudin; Keeeun Lee; Ganjar Alfian; Jaeho Lee; Jongtae Rhee. Application of Bluetooth Low Energy-Based Real-Time Location System for Indoor Environments. Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things - BDIOT 2018 2018, 167 -171.
AMA StyleMuhammad Syafrudin, Keeeun Lee, Ganjar Alfian, Jaeho Lee, Jongtae Rhee. Application of Bluetooth Low Energy-Based Real-Time Location System for Indoor Environments. Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things - BDIOT 2018. 2018; ():167-171.
Chicago/Turabian StyleMuhammad Syafrudin; Keeeun Lee; Ganjar Alfian; Jaeho Lee; Jongtae Rhee. 2018. "Application of Bluetooth Low Energy-Based Real-Time Location System for Indoor Environments." Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things - BDIOT 2018 , no. : 167-171.
Currently, the manufacturing industry is experiencing a data-driven revolution. There are multiple processes in the manufacturing industry and will eventually generate a large amount of data. Collecting, analyzing and storing a large amount of data are one of key elements of the smart manufacturing industry. To ensure that all processes within the manufacturing industry are functioning smoothly, the big data processing is needed. Thus, in this study an open source-based real-time data processing (OSRDP) architecture framework was proposed. OSRDP architecture framework consists of several open sources technologies, including Apache Kafka, Apache Storm and NoSQL MongoDB that are effective and cost efficient for real-time data processing. Several experiments and impact analysis for manufacturing sustainability are provided. The results showed that the proposed system is capable of processing a massive sensor data efficiently when the number of sensors data and devices increases. In addition, the data mining based on Random Forest is presented to predict the quality of products given the sensor data as the input. The Random Forest successfully classifies the defect and non-defect products, and generates high accuracy compared to other data mining algorithms. This study is expected to support the management in their decision-making for product quality inspection and support manufacturing sustainability.
Muhammad Syafrudin; Norma Latif Fitriyani; Donglai Li; Ganjar Alfian; Jongtae Rhee; Yong-Shin Kang. An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability. Sustainability 2017, 9, 2139 .
AMA StyleMuhammad Syafrudin, Norma Latif Fitriyani, Donglai Li, Ganjar Alfian, Jongtae Rhee, Yong-Shin Kang. An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability. Sustainability. 2017; 9 (11):2139.
Chicago/Turabian StyleMuhammad Syafrudin; Norma Latif Fitriyani; Donglai Li; Ganjar Alfian; Jongtae Rhee; Yong-Shin Kang. 2017. "An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability." Sustainability 9, no. 11: 2139.
Since customer attention is increasing due to growing customer health awareness, it is important for the perishable food supply chain to monitor food quality and safety. This study proposes a real-time monitoring system that utilizes smartphone-based sensors and a big data platform. Firstly, we develop a smartphone-based sensor to gather temperature, humidity, GPS, and image data. The IoT-generated sensor on the smartphone has characteristics such as a large amount of storage, an unstructured format, and continuous data generation. Thus, in this study, we propose an effective big data platform design to handle IoT-generated sensor data. Furthermore, the abnormal sensor data generated by failed sensors is called outliers and may arise in real cases. The proposed system utilizes outlier detection based on statistical and clustering approaches to filter out the outlier data. The proposed system was evaluated for system and gateway performance and tested on the kimchi supply chain in Korea. The results showed that the proposed system is capable of processing a massive input/output of sensor data efficiently when the number of sensors and clients increases. The current commercial smartphones are sufficiently capable of combining their normal operations with simultaneous performance as gateways for transmitting sensor data to the server. In addition, the outlier detection based on the 3-sigma and DBSCAN were used to successfully detect/classify outlier data as separate from normal sensor data. This study is expected to help those who are responsible for developing the real-time monitoring system and implementing critical strategies related to the perishable supply chain.
Ganjar Alfian; Muhammad Syafrudin; Jongtae Rhee. Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain. Sustainability 2017, 9, 2073 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Jongtae Rhee. Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain. Sustainability. 2017; 9 (11):2073.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Jongtae Rhee. 2017. "Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain." Sustainability 9, no. 11: 2073.
Ganjar Alfian; Jongtae Rhee; Hyejung Ahn; Jaeho Lee; Umar Farooq; Muhammad Fazal Ijaz; M. Alex Syaekhoni. Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering 2017, 212, 65 -75.
AMA StyleGanjar Alfian, Jongtae Rhee, Hyejung Ahn, Jaeho Lee, Umar Farooq, Muhammad Fazal Ijaz, M. Alex Syaekhoni. Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering. 2017; 212 ():65-75.
Chicago/Turabian StyleGanjar Alfian; Jongtae Rhee; Hyejung Ahn; Jaeho Lee; Umar Farooq; Muhammad Fazal Ijaz; M. Alex Syaekhoni. 2017. "Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system." Journal of Food Engineering 212, no. : 65-75.
Intelligent Food Packaging, Distribution Management System tracks the real-time location and temperature information through the food distribution process and optimizes the supply chain in order to satisfy the consumers who want to purchase high quality food. This system uses RFID, sensor network, temperature-history indicator, smart device, etc. to collect information necessary for management of distribution. To develope system, hardware costs, software costs, and labor expenses should be invested. Furthermore, the effects of system can be calculated quantitatively. The purpose of this study is to analyze the actual cost and benefit of Gyeonggi Agricultural Cooperative Federation’s school meal Kimchi supply chain. Cost-benefit analysis is conducted on the different scenarios, and they differ depending on the depth of interest in traceability of different supply chain participants. As a result of this study, we hope to build an environment that can provide transparent food information to different participants of supply chain, support successful supply chain management, and finally improve all participants’satisfaction.
Hyejeong An; Bohan Yoon; Heejae Han; Jaeho Lee; Ganjar Alfian; Jongtae Rhee. Study on Cost-Benefit Analysis of Intelligent Food Packaging, Distribution Management System for Kimchi logistics in School food Service Scenario. Journal of the Korean Society of Supply Chain Management 2017, 17, 155 -163.
AMA StyleHyejeong An, Bohan Yoon, Heejae Han, Jaeho Lee, Ganjar Alfian, Jongtae Rhee. Study on Cost-Benefit Analysis of Intelligent Food Packaging, Distribution Management System for Kimchi logistics in School food Service Scenario. Journal of the Korean Society of Supply Chain Management. 2017; 17 (2):155-163.
Chicago/Turabian StyleHyejeong An; Bohan Yoon; Heejae Han; Jaeho Lee; Ganjar Alfian; Jongtae Rhee. 2017. "Study on Cost-Benefit Analysis of Intelligent Food Packaging, Distribution Management System for Kimchi logistics in School food Service Scenario." Journal of the Korean Society of Supply Chain Management 17, no. 2: 155-163.
A carsharing service can be seen as a transport alternative between private and public transport that enables a group of people to share vehicles based at certain stations. The advanced carsharing service, one-way carsharing, enables customers to return the car to another station. However, one-way implementation generates an imbalanced distribution of cars in each station. Thus, this paper proposes forecasting relocation to solve car distribution imbalances for one-way carsharing services. A discrete event simulation model was developed to help evaluate the proposed model performance. A real case dataset was used to find the best simulation result. The results provide a clear insight into the impact of forecasting relocation on high system utilization and the reservation acceptance ratio compared to traditional relocation methods.
Ganjar Alfian; Jongtae Rhee; Muhammad Fazal Ijaz; Muhammad Syafrudin; Norma Latif Fitriyani. Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing. Applied Sciences 2017, 7, 598 .
AMA StyleGanjar Alfian, Jongtae Rhee, Muhammad Fazal Ijaz, Muhammad Syafrudin, Norma Latif Fitriyani. Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing. Applied Sciences. 2017; 7 (6):598.
Chicago/Turabian StyleGanjar Alfian; Jongtae Rhee; Muhammad Fazal Ijaz; Muhammad Syafrudin; Norma Latif Fitriyani. 2017. "Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing." Applied Sciences 7, no. 6: 598.
Sustainability relies on the environmental, social and economical systems: the three pillars of sustainability. The social sustainability mostly advocates the people’s welfare, health, safety, and quality of life. In the agricultural food industry, the aspects of social sustainability, such as consumer health and safety have gained substantial attention due to the frequent cases of food-borne diseases. The food-borne diseases due to the food degradation, chemical contamination and adulteration of food products pose a serious threat to the consumer’s health, safety, and quality of life. To ensure the consumer’s health and safety, it is essential to develop an efficient system which can address these critical social issues in the food distribution networks. This research proposes an ePedigree (electronic pedigree) traceability system based on the integration of RFID and sensor technology for real-time monitoring of the agricultural food to prevent the distribution of hazardous and adulterated food products. The different aspects regarding implementation of the proposed system in food chains are analyzed and a feasible integrated solution is proposed. The performance of the proposed system is evaluated and finally, a comprehensive analysis of the proposed ePedigree system’s impact on the social sustainability in terms of consumer health and safety is presented.
Umar Farooq; Wu Tao; Ganjar Alfian; Yong-Shin Kang; Jongtae Rhee. ePedigree Traceability System for the Agricultural Food Supply Chain to Ensure Consumer Health. Sustainability 2016, 8, 839 .
AMA StyleUmar Farooq, Wu Tao, Ganjar Alfian, Yong-Shin Kang, Jongtae Rhee. ePedigree Traceability System for the Agricultural Food Supply Chain to Ensure Consumer Health. Sustainability. 2016; 8 (9):839.
Chicago/Turabian StyleUmar Farooq; Wu Tao; Ganjar Alfian; Yong-Shin Kang; Jongtae Rhee. 2016. "ePedigree Traceability System for the Agricultural Food Supply Chain to Ensure Consumer Health." Sustainability 8, no. 9: 839.
A car sharing service has been highlighted as a new urban transport alternative for an environmentally friendly economy. As the demand for the service from customers increases, car sharing operators need to introduce a new service such as a one-way option that will allow customers to return the car to different stations. Due to the complexity of the one-way system, it needs to be managed and optimized for real cases. This paper focuses on developing a simulation model in order to help operators evaluate the performance of the one-way service. In addition, this research demonstrates a strategy for an open one-way service that can increase revenue and customer satisfaction. A real case dataset is used for investigation to find the best result from the simulation. The result showed that the total number of cars, number of one-way reservations and station size have an impact on one-way performance. Thus, company profit and customer satisfaction can be maximized by optimizing these factors.
Ganjar Alfian; Jongtae Rhee; Yong-Shin Kang; ByungUn Yoon. Performance Comparison of Reservation Based and Instant Access One-Way Car Sharing Service through Discrete Event Simulation. Sustainability 2015, 7, 12465 -12489.
AMA StyleGanjar Alfian, Jongtae Rhee, Yong-Shin Kang, ByungUn Yoon. Performance Comparison of Reservation Based and Instant Access One-Way Car Sharing Service through Discrete Event Simulation. Sustainability. 2015; 7 (9):12465-12489.
Chicago/Turabian StyleGanjar Alfian; Jongtae Rhee; Yong-Shin Kang; ByungUn Yoon. 2015. "Performance Comparison of Reservation Based and Instant Access One-Way Car Sharing Service through Discrete Event Simulation." Sustainability 7, no. 9: 12465-12489.
A carsharing service is a form of public transportation that enables a group of people to share vehicles based at certain stations by making reservations in advance. One of the common problems of carsharing is that companies can have difficulty optimizing the number of vehicles in operation. This paper reports on investigations of the relationship between the number of cars and the number of reservations per day with either the acceptance ratio or utilization ratio based on the commerciallyoperational dataset of a carsharing company in Korea. A discrete event simulation is run to analyze a round-trip service for every possible number of cars and number of reservations with the output acceptance ratio and utilization ratio. The simulation data revealed that increasing the number of reservations with respect to a certain number of cars will decrease the acceptance ratio, thus increasing the percentage of the utilization ratio. Based on the simulation data results, a rational regression model can achieve high precision when predicting the acceptance ratio or the utilization ratio compared to other prediction algorithms such as the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF) models. K-means clustering was used to understand the pattern and provide additional policies for carsharing companies. Consequently, opening a carsharing business is very promising in terms of profit, escalating the level of customer satisfaction. In addition, a small reduction in the utilization ratio by operators will create a large increase in the acceptance ratio.
Jongtae Jongtae Rhee, Department of Industrial & Systems Engineering, Dongguk University; Ganjar Alfian; ByungUn Byungun Yoon, Department of Industrial & Systems Engineering, Dongguk University. Application of Simulation Method and Regression Analysis to Optimize Car Operations in Carsharing Services: A Case Study in South Korea. Journal of Public Transportation 2014, 17, 121 -160.
AMA StyleJongtae Jongtae Rhee, Department of Industrial & Systems Engineering, Dongguk University, Ganjar Alfian, ByungUn Byungun Yoon, Department of Industrial & Systems Engineering, Dongguk University. Application of Simulation Method and Regression Analysis to Optimize Car Operations in Carsharing Services: A Case Study in South Korea. Journal of Public Transportation. 2014; 17 (1):121-160.
Chicago/Turabian StyleJongtae Jongtae Rhee, Department of Industrial & Systems Engineering, Dongguk University; Ganjar Alfian; ByungUn Byungun Yoon, Department of Industrial & Systems Engineering, Dongguk University. 2014. "Application of Simulation Method and Regression Analysis to Optimize Car Operations in Carsharing Services: A Case Study in South Korea." Journal of Public Transportation 17, no. 1: 121-160.
To a customer, the waiting time for order processing for a product or service is important information for order placement. If the time foreseen for order fulfillment is long, the order might be lost to a competitor. In particular, modern principles of supply chain management highly suggest information sharing between entities in the chain and information technology has enabled customers to conveniently consider the waiting time for a potential balking decision. To help determine the design and operation of a manufacturing or service system in which a customer may balk based on the foreseen waiting time, this paper develops procedures to estimate the average waiting time of an order. Either the procedures allow the maximum waiting time for a balking decision to be random or do not require knowledge of the arrival process of customers before balking if the balking limit is known. For generality of the model, this paper considers general inter-arrival and service time distributions, and uses the simulation and regression approach.
Jaejin Jang; Jaewoo Chung; Jungdae Suh; Jongtae Rhee. Estimation of the mean waiting time of a customer subject to balking: a simulation study. International Journal of Flexible Manufacturing Systems 2007, 18, 121 -144.
AMA StyleJaejin Jang, Jaewoo Chung, Jungdae Suh, Jongtae Rhee. Estimation of the mean waiting time of a customer subject to balking: a simulation study. International Journal of Flexible Manufacturing Systems. 2007; 18 (2):121-144.
Chicago/Turabian StyleJaejin Jang; Jaewoo Chung; Jungdae Suh; Jongtae Rhee. 2007. "Estimation of the mean waiting time of a customer subject to balking: a simulation study." International Journal of Flexible Manufacturing Systems 18, no. 2: 121-144.
Recently, many production systems are being multi-nationalized by the globalization of industries. Based on this worldwide trend, many enterprises of Korea (the South) advanced into the North Korea (the North) and they could enjoy economic advantages such as cheap but excellent labor force and inexpensive price of land. But the reality is that they could not made the most use of such advantages because of the inefficient operation of supply chain and the slow administration process of the North. This paper explores the practical problems of supply chain management (SCM) of the enterprises that advanced into the North and suggests an improved SCM plan to deal with such problems. The experience of strategic planning of H logistics company (HLC), a well-coordinated logistic company in the South, is introduced as the case study.
Yeong Joon Yoo; Jong Tae Rhee. An application of SCM-based logistics planning in the trade between South and North Korea. Computers & Industrial Engineering 2002, 43, 159 -168.
AMA StyleYeong Joon Yoo, Jong Tae Rhee. An application of SCM-based logistics planning in the trade between South and North Korea. Computers & Industrial Engineering. 2002; 43 (1-2):159-168.
Chicago/Turabian StyleYeong Joon Yoo; Jong Tae Rhee. 2002. "An application of SCM-based logistics planning in the trade between South and North Korea." Computers & Industrial Engineering 43, no. 1-2: 159-168.