• Journal of Bio-Environment Control
• /
• v.6 no.3
• /
• pp.183-189
• /
• 1997

This study was performed to Investigate size of droplet sprinkled from mini-sprinkler. Twelve different kinds of the sprinkler having various structures and sizes of nozzle orifices were selected and tested. Diameters of the droplet reached at several distances from a sprinkler were measured by a machine vision system and the volume median diameters (VMM) were determined statistically. The size of droplet was not affected much by the size of nozzle orifice of a sprinkler but was rather more affected by structure of the sprinkler, especially by the shape of spreader of the sprinkler. Experiment of varying pressure of sprinkling water validated that the size of droplet was inversely proportional to water pressure powered by 1/3. Hence the size of droplet at any water pressure could be easily estimated from experimental data. The size of droplet increased as travel distance of the droplet increases in a relationship of and order function. The size of droplet of the tested sprinkler were in the ranges of 100-300fm within 1m of droplet travel distance, 230~470${\mu}{\textrm}{m}$ within 1~2m of droplet travel distance and 300~770${\mu}{\textrm}{m}$ within 2~3m of droplet travel distance.

• The KIPS Transactions:PartB
• /
• v.9B no.2
• /
• pp.245-254
• /
• 2002

IC package marking inspection system using machine vision locates and identifies the target elements from input image, and decides the quality of marking by comparing the extracted target elements with the standard patterns. This paper proposes an adaptive multi-level thresholding (AMLT) method which is suitable for a series of operations such as locating the target IC package, extracting the characters, and detecting the Pinl dimple. It also proposes a dynamic matching unit selection (DMUS) method which is robust to noises as well as effective to catch out the local marking errors. The main idea of the AMLT method is to restrict the inputs of Otsu's thresholding algorithm within a specified area and a partial range of gray values. Doing so, it can adapt to the specific domain. The DMUS method dynamically selects the matching unit according to the result of character extraction and layout analysis. Therefore, in spite of the various erroneous situation occurred in the process of character extraction and layout analysis, it can select minimal matching unit in any environment. In an experiment with 280 IC package images of eight types, the correct extracting rate of IC package and Pinl dimple was 100% and the correct decision rate of marking quality was 98.8%. This result shows that the proposed methods are effective to IC package marking inspection.

• Journal of Digital Contents Society
• /
• v.15 no.4
• /
• pp.481-489
• /
• 2014

In this paper, the automatic standby power blocking socket outlet to reduce standby power by blocking power threshold is implemented. Where, the standby power means a flowing power when a disused power electronic is plugged into the socket outlet. The proposed socket outlet can cut off the standby power by establishing a proper block power threshold electronic device according to each electronic device because it can monitor the amount of power through the smart machines such as the real-time PC or mobile phone and directly control the blocking power threshold. The software is implemented by using Visual Studio software, code vision and SN8 C studio, and the hardware is embodied in ATmega128, SN8F27E93S, USB to UART, and relay etc. Through the simulation, we find that the standby power of the proposed method is similar to that of the conventional method in case of the cellular phone but the standby power of the proposed method is much less than that of the conventional method in case of the computer, air conditioning, and set-top box. Therefore, it is proved that the proposed socket outlet has a superior performance in terms of the standby power.

• Journal of Korean society of Dental Hygiene
• /
• v.8 no.4
• /
• pp.19-30
• /
• 2008

This study reached the following conclusions as a result of carrying out the questionnaire survey of self-descriptions for the satisfaction after the citizens of Daejon uses the dental clinics, in order to identify the factors of satisfaction to the medical services of such dental clinics to be utilized in the patient management by dental hygienists, provide the basic data to provide the medical services desired by patients. 1. 43.9% men responded to the facilities and 56.1% women to the atmosphere for the standards of selection of dental clinics by general characteristic, and the college graduates or more to the kindness (38.2%), high-school graduates (43.2%) and middle-school graduates (25.9%) or less to the close distance for the level of educational attainment (p=0.009), which was meant to have a statistical significance. 2. The execution of reservation system for the dental clinics showed 54.7%, the reserved time was observed upon the execution of such reservation system, the dental clinics where they practice such system were 40.6%, and the confirmation methods was done through the telephone with 62.5%. 3. The experience of fear upon the dental treatment showed 74.6%. The type of fear showed the machine sound (48.7%) for men and cry of others for women (70.8%) at the highest. 70% of those under 30 at the age responded to the sharp instruments at the highest. 83.3% of Yousung-gu showed the highest by responding to the cry of others for the residential areas. The statistically significant difference was shown in both the age and residential area (p=0.000). 4. Women showed higher in the distribution of gender for the sterilization of instruments for the external satisfaction of dental clinics(p=0.000) and those under 30 at the age showed the highest with 2.98${\pm}$0.95(p=0.001). Seo-gu (3.48${\pm}$0.77) was the highest for the residential area (p=0.000), and there was statistically significant differences in the gender, age and residential area. 5. Men showed higher satisfaction than women in the clean state and the statistically significant differences were shown (p=0.000) at the age as the high satisfaction was shown for those under 30 at the age (2.35${\pm}$0.79), those having the income not less than 10 million won and not more than 20 million won (2.43${\pm}$0.78), and Seo-gu (2.63 ${\pm}$0.69) for the residential area. 6. For the internal satisfaction of dental clinic by users for the medical services in the dental clinics, 61.1% women responded to no in the ability of solving the inconvenience in the service process, and showed low ability of solving the inconvenience from 30 at the age (26.2%) and by responding to Dong-gu (22.1%) for the residential area, showing statically significant differences(p=0.000). For the re-use of dental clinics, 46.6% men (p=0.043) for the gender, 24.3% under 30 at the age and 22.9% of Dong-gu for the residential area responded to the re-use, showing statistically significant differences for the gender and residential area (p=0.000). 7. The dissatisfaction showed a high rate of 69.5% for the satisfaction to the medical services of dental clinics. 46.2% men responded to the pain and women to the feeling of foreign substance for the reason of dissatisfaction while those under 30 at the age showed 55.6% for others, those between 50 and 59 41.7% for the feeling of foreign substance. 86.3% carried out the education for cautions after the treatments and most people turned out that they do not carry out the continuous health management of mouth as 20.5% responded to that they carry out such health management.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.89-106
• /
• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Journal of Intelligence and Information Systems
• /
• v.26 no.3
• /
• pp.71-90
• /
• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.1-19
• /
• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.