• The KIPS Transactions:PartB
• /
• 제15B권6호
• /
• pp.575-584
• /
• 2008

According to the network condition, the communication network overload could be occurred when media transmitting. Many researches are being carried out to lessen the network overload, such as the filtering, load distributing, frame dropping and many other methods. Among these methods, one of effective method is frame dropping that reduces specified video frames for bandwidth diminution. B frames are dropped and then I, P frames are dropped according to dependency among the frames in frame dropping. This paper proposes a scheme for protecting copyrights by encryption, when we apply frame dropping to reduce bandwidth of media following MPEG-4 file format. We designed two kinds of frame dropping: first one stores and then sends the dropped files and the other drops frames in real-time when transmitting. We designed three kinds of encryption methods in which DES algorithm is used to encrypt MPEG-4 data: macro block encryption in I-VOP, macro block and motion vector encryption in P-VOP, and macro block and motion vector encryption in I, P-VOP. Based on these three methods, we implemented a digital right management solution for MPEG-4 data streaming. We compared the results of dropping, encryption, decryption and quality of video sequences to select an optimal method, and there is no noticeable difference between the video sequences recovered after frame dropping and the ones recovered without frame dropping. The best performance in encryption and decryption of frames was obtained when we apply the macro block and motion vector encryption in I, P-VOP.

• The KIPS Transactions:PartB
• /
• 제15B권1호
• /
• pp.45-52
• /
• 2008

As the recent development of the IT services, there is a urgent need of effective diagnosis system to present appropriate solution for the complicated problems of breakdown control, a cause analysis of breakdown and others. So we propose an intelligent diagnosis system that integrates the case-based reasoning and the artificial neural network to improve the system performance and to achieve optimal diagnosis. The case-based reasoning is a reasoning method that resolves the problems presented in current time through the past cases (experience). And it enables to make efficient reasoning by means of less complicated knowledge acquisition process, especially in the domain where it is difficult to extract formal rules. However, reasoning by using the case-based reasoning alone in diagnosis problem domain causes a problem of suggesting multiple causes on a given symptom. Since the suggested multiple causes of given symptom has the same weight, the unnecessary causes are also examined as well. In order to resolve such problems, the back-propagation learning algorithm of the artificial neural network is used to train the pairs of the causes and associated symptoms and find out the cause with the highest weight for occurrence to make more clarified and reliable diagnosis.

• The Journal of the Convergence on Culture Technology
• /
• 제5권1호
• /
• pp.353-360
• /
• 2019

Trade credit is being used as a price discrimination strategy by the suppliers in order to increase the customer's demand. From the viewpoint of the customer, if delayed payment is allowed for a certain period of time from the supplier, the effect of reducing the inventory carrying cost will positively affect the customer's order quantity. Also, in deriving the economic order quantity(EOQ) formula, it is tacitly assumed that the customer's ordering cost is a fixed cost. However in many business transactions, the customer pays the freight cost for the transportation of his order and so, the customer's ordering cost contains not only a fixed cost but also a freight cost which is a function of the order size. Therefore, in this study, we analyzed the inventory model which considers that the customer's ordering cost contains not only a fixed cost but also a freight cost which is a function of the customer's order size when the supplier permits a delay in payments. For the analysis, it is also assumed that inventory is exhausted not only by customer's demand but also by deterioration. Investigation of the properties of an optimal solution allows us to develop an algorithm whose validity is illustrated using an example problem.

• Journal of Intelligence and Information Systems
• /
• 제27권3호
• /
• pp.231-252
• /
• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.