• Convergence Security Journal
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• v.24 no.3
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• pp.81-93
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• 2024

The purpose of this paper is to derive considerations when improving the Personal Information Protection Act based on the personal information protection life cycle of the generative artificial intelligence model as generative artificial intelligence models are introduced and used in Korea a lot. Through the study, the necessity of using open information in the collection stage, using personal information preservation technology in the learning stage, and preparing the basis for the development of protection technology in the holding stage was derived. It also revealed the necessity of managing the generated information in the generation and inference stage, re-learning in the limitation and destruction stage, and preparing a filtering basis. It is expected that the results of this study can be used to revise the Personal Information Protection Act and make policies in the future.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.180-188
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• 1999

The objective of this paper is, based upon the principles of artificial life, to induce emergent behaviors of multiple autonomous mobile robots which complex global intelligence form from simple local interactions. Here, we propose an architecture of neural network learning with reinforcement signals which perceives the neighborhood information and decides the direction and the velocity of movement as mobile robots navigate in a group. As the results of the simulations, the optimum weight is obtained in real time, which not only prevent the collisions between agents and obstacles in the dynamic environment, but also have the mobile robots move and keep in various patterns.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.74-81
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• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA(Dropout Genetic Algorithm) which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.21-27
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• 2023

This paper discusses a deep learning-based road surface analysis system that collects data by installing vibration sensors on the 4-axis wheel bearings of a vehicle, analyzes the data, and appropriately classifies the characteristics of the current driving road surface for use in the vehicle's control system. The data used for road surface analysis is real-time large-capacity data, with 48K samples per second, and the A2B protocol, which is used for large-capacity real-time data communication in modern vehicles, was used to collect the data. CAN and CAN-FD commonly used in vehicle communication, are unable to perform real-time road surface analysis due to bandwidth limitations. By using A2B communication, data was collected at a maximum bandwidth for real-time analysis, requiring a minimum of 24K samples/sec for evaluation. Based on the data collected for real-time analysis, performance was assessed using deep learning models such as LSTM, GRU, and RNN. The results showed similar road surface classification performance across all models. It was also observed that the quality of data used during the training process had an impact on the performance of each model.

• Journal of Venture Innovation
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• v.7 no.2
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• pp.143-152
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• 2024

Recently, as a lot of software with AI functions has been developed, the number of software products with various prediction functions is increasing, and as a result, the importance of software quality has increased. In particular, as consideration for functional safety of products with AI functions increases, software quality management is being conducted at a national level. In particular, the GS Quality Certification System is a quality certification system for software products that is being implemented at the national level, and the GS Certification System is also researching quality evaluation methods for AI products. In this study, we attempt to present an evaluation model that satisfies the basic conditions of software quality based on international standards among the various quality evaluation models presented to verify software reliability. Considering the software quality characteristics of the artificial intelligence sector, we study quality evaluation models, diagnose quality, and predict failures. .In this study, we propose an international standard model for artificial intelligence based on the software reliability growth model, present an evaluation model, and present a method for quality diagnosis through the model. In this respect, this study is considered to be important in that it can predict failures in advance and find failures in advance to prevent risks by predicting the failure time that will occur in software in the future. In particular, it is believed that predicting failures will be important in various safety-related software.

• Journal of The Korean Association of Information Education
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• v.24 no.4
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• pp.279-289
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• 2020

The purpose of this study is to apply the training program for teachers to strengthen the competence of artificial intelligence education in primary and secondary school teachers and to analyze its effectiveness and analyze teachers' demands for artificial intelligence education to provide basic research data. The referenced training program was designed based on the ADDIE model by selecting the educational contents based on the five core elements of AI, and teachers from the G Metropolitan Office of Education and the AI Education Research Association collaborated to develop the program. The effectiveness of the developed program and questionnaire of teacher needs analysis for AI teaching were examined for content validity. As a result of the training conducted by applying the developed program, satisfaction with each curriculum of the training and the possibility of application to the field were highly evaluated. It was found that teachers consider the need of teaching unplugged activities for AI education and basic AI experiences in elementary school level, and AI education contents including block programming languages and physical computing activities are needed to teach in middle school level.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.134-139
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• 2023

Rehabilitators perform outpatient treatment and daily rehabilitation exercises to recover physical function with the aim of quickly returning to society after surgical treatment. Unlike performing exercises in a hospital with the help of a professional therapist, there are many difficulties in performing rehabilitation exercises by the patient on a daily basis. In this paper, we propose a CNN-LSTM-based upper limb rehabilitation real-time monitoring system so that patients can perform rehabilitation efficiently and with correct posture on a daily basis. The proposed system measures biological signals through shoulder-mounted hardware equipped with EMG and IMU, performs preprocessing and normalization for learning, and uses them as a learning dataset. The implemented model consists of three polling layers of three synthetic stacks for feature detection and two LSTM layers for classification, and we were able to confirm a learning result of 97.44% on the validation data. After that, we conducted a comparative evaluation with the Teachable machine, and as a result of the comparative evaluation, we confirmed that the model was implemented at 93.6% and the Teachable machine at 94.4%, and both models showed similar classification performance.

• Smart Media Journal
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• v.13 no.1
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• pp.24-35
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• 2024

The combination of Artificial Intelligence and the Internet of Things (AIoT) has gained significant popularity. Deep neural networks (DNNs) have demonstrated remarkable success in various applications. However, deploying complex AI models on embedded boards can pose challenges due to computational limitations and model complexity. This paper presents an AIoT-based system for smart parking lots using edge devices. Our approach involves developing a detection model and a decision tree for occupancy status classification. Specifically, we utilize YOLOv5 for car license plate (LP) detection by verifying the position of the license plate within the parking space.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.93-102
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• 2022

Dependency parsing is a decision problem of the syntactic relation between words in a sentence. Recently, deep learning models are used for dependency parsing based on the word representations in a continuous vector space. However, it causes a mislabeled tagging problem for the proper nouns that rarely appear in the training corpus because it is difficult to express out-of-vocabulary (OOV) words in a continuous vector space. To solve the OOV problem in dependency parsing, we explored the proper noun embedding method according to the embedding unit. Before representing words in a continuous vector space, we replace the proper nouns with a special token and train them for the contextual features by using the multi-layer bidirectional LSTM. Two models of the syllable-based and morpheme-based unit are proposed for proper noun embedding and the performance of the dependency parsing is more improved in the ensemble model than each syllable and morpheme embedding model. The experimental results showed that our ensemble model improved 1.69%p in UAS and 2.17%p in LAS than the same arc-eager approach-based Malt parser.