• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.13-28
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• 2021

Reinforcement Learning (RL) is a machine learning algorithm that repeat the closed-loop process that agents perform actions specified by the policy, the action is evaluated with a reward function, and the policy gets updated accordingly. The key benefit of RL is the ability to optimze the policy with action evaluation. Hence, it can effectively be applied to developing advanced intelligent systems and autonomous systems. Conventional RL incoporates a single policy, a reward function, and relatively simple policy update, and hence its utilization was limited. In this paper, we propose an extended RL model that considers multiple instances of RL elements. We define a formal model of the key elements and their computing model of the extended RL. Then, we propose design methods for applying to system development. As a case stud of applying the proposed formal model and the design methods, we present the design and implementation of an advanced car navigator system that guides multiple cars to reaching their destinations efficiently.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.118-133
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• 2020

Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.912-922
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• 2009

By 'National GIS Plan', various strategies and programs have been carried out for the professional training in the fields of geospatial information. The e-learning can make it possible to develop and manage the adaptable curriculums, to maximize the effect of the practical exercises, and to establish the cooperative systems with the industries. In this study, curriculums of the geospatial fields were developed and suggested for the cyber universities. These curriculums were divided into three stages, fundamentals, applications, and advances, and into three tracks, system development and construction, mapping and geospatial data construction, and practice and application, based on the current demands in geospatial industries. Owing to be the modularized structure, proposed curriculums would be easily adapted and updated to the change of the new demands.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1348-1375
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• 2018

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2232-2238
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• 2008

Most questions made for remote examinations on E-learning databank based selection system use methods of making questions automatically using degrees of difficulty. This method is the kernel of a question selection that degrees of difficulty as make test questions, and then needs continuous management for degrees of difficulty. This paper presents improved algorithms for dynamically adjustment of degrees of difficulty based on examination result that is more efficient sot of questions. We identified this algorithm is more effective as compared with previous algorithms on web-based education systems.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.117-127
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• 2023

In many training institutions, the major advancement of Information Technology is having a profound impact on the way in which instructors teach and students learn, as well as how the two interact. The training process is continuing with the goal of enhancing the calibre of instruction and engagement. Top colleges and institutions have more recently developed a variety of Massive Open Online Courses (MOOC) systems centred on the development of new educational offering ways. These have not only captured the interest of students and scholars in the field of higher education, but also that of staff members in the private and public sectors. This study uses a Unified Theory of Acceptance and Use of Technology (UTAUT) model to assess the top MOOC providers and pinpoint the key elements influencing learner acceptance of MOOCs in Saudi Arabian training. A total of 382 government trainees in Saudi Arabia participated in an online survey, the results of which underwent analysis using structural equation modelling. This study identifies the key elements influencing Saudi government employee trainees' intentions to use MOOCs, with the findings indicating that the suggested model can account for 86.2% of user behaviour and 88.5% of user intentions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.96-101
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• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.259-263
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• 2008

임베디드 기기에서 로그데이터란 사용자의 기기 사용 이력에 대한 하드웨어적인 기록이라 할 수 있고, 로그분석이란 이 로그데이터를 기반으로 다양한 정보를 추출해 내는 것이다. 하지만 기존 로그데이터는 사용자의 행위에 대한 모든 기록에 대한 나열에 그쳤기 때문에 실제 사용자 행동 패턴이나 사용성에 대한 분석을 하기 위해서는 방대한 로그데이터를 활용하는데 많은 어려움이 있었다. 이에 본 논문은 이러한 사용자의 행동에 대한 체계적인 분석과 임베디드 기기 S/W와 H/W 연동성을 높이기 위하여 새로운 로그데이터 포맷에 대한 연구를 수행하였다. 이는 다양한 임베디드 기기의 분석을 위한 효율성과 효과성을 증대하는데 기여할 것이다.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1205-1212
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• 2004

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of adaptive e-commerce agents can monitor customer's behaviors and cluster thou in similar categories, and include user's preference from each category. In order to implement our adaptive e-commerce agent system, in this paper, we propose an adaptive e-commerce agent systems consider customer's information of interest and goodwill ratio about preference goods. Proposed system build user's profile more accurately to get adaptability for user's behavior of buying and provide useful product information without inefficient searching based on such user's profile. The proposed system composed with three parts , Monitor Agent which grasps user's intension using monitoring, similarity reference Agent which refers to similar group of behavior pattern after teamed behavior pattern of user, Interest Analyzing Agent which personalized behavior DB as a change of user's behavior.