• Korean Medical Education Review
• /
• v.22 no.2
• /
• pp.99-114
• /
• 2020

The rapid development of artificial intelligence (AI), including deep learning, has led to the development of technologies that may assist in the diagnosis and treatment of diseases, prediction of disease risk and prognosis, health index monitoring, drug development, and healthcare management and administration. However, in order for AI technology to improve the quality of medical care, technical problems and the efficacy of algorithms should be evaluated in real clinical environments rather than the environment in which algorithms are developed. Further consideration should be given to whether these models can improve the quality of medical care and clinical outcomes of patients. In addition, the development of regulatory systems to secure the safety of AI medical technology, the ethical and legal issues related to the proliferation of AI technology, and the impacts on the relationship with patients also need to be addressed. Systematic training of healthcare personnel is needed to enable adaption to the rapid changes in the healthcare environment. An overall review and revision of undergraduate medical curriculum is required to enable extraction of significant information from rapidly expanding medical information, data science literacy, empathy/compassion for patients, and communication among various healthcare providers. Specialized postgraduate AI education programs for each medical specialty are needed to develop proper utilization of AI models in clinical practice.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.10a
• /
• pp.530-535
• /
• 2002

Pneumatic control system has been applied to build many industrial automation systems. But most of them are sequence control type because of their low costs, safety, reliability, etc. Pneumatic servo system is rarely applied to real industrial fields because accurate position control is very difficult due to its nonlinearity and compressibility of air. In pneumatic servo control system, a pneumatic servo valve can be applied, But it is very expensive and has no advantage of low cost compared with a common pneumatic system. This paper is concerned with the accurate position control of a rodless pneumatic cylinder using on/off solenoid valve. A novel Intelligent Modified Pulse Width Modulation(MPWM) is newly proposed. The control performance of this pneumatic cylinder depends on the external loads. To overcome this problem, switching of control parameter using artificial neural network is newly proposed, which estimates external loads on rodless pneumatic cylinder using this training neural network. As an underlying controller, a state feedback controller using position, velocity and acceleration is applied in the switching control the system. The effectiveness of the proposed control algorithms are demonstrated through experiments nth various loads.

• International Journal of Quality Innovation
• /
• v.3 no.2
• /
• pp.159-173
• /
• 2002

Any organization's Six Sigma program may be at high risk without heeding the lessons learned from the past and that tries to operate without a robust business foundation. A foundation that preferably should consist of stepping-stones such as a 5-S house-keeping program, an effective Integrated Management System (IMS), which includes a strong focus on planning for quality to fully capture the Voice of the Customer (VOC), and an organization-wide training scheme, as well as a reliable Cost of Poor Quality (COPQ) system. That's the best advise I can give to any organization that wishes to embark on a Six Sigma improvement program and hope to be successful. The paper will elaborate on the above issues and provide suggested solutions based on the review of published historical information and the experiences encountered over the last four decades by the author, as a quality practitioner and consultant, in industries that produced safety-critical product. This author maintains that few fundamentally new or useful things have been created in the field of Quality during the last couple of decades. Nevertheless, this paper deliberates on a number of relatively “newer” issues including the concept of “three types of customers”, the CTC, “Critical To Customer” term, the eight Quality Management Principles of the new ISO 9000 family, the growth of industry-specific standards, the adoption of Integrated Management Systems, the rebirth of AS2561 COQ standard, the spread of Six Sigma as well as related ASQ certification and the need for a robust business foundation to ensure Six Sigma survival.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.2
• /
• pp.600-616
• /
• 2021

SIMON and SPECK are two families of lightweight block ciphers that have excellent performance on hardware and software platforms. At CRYPTO 2019, Gohr first introduces the differential cryptanalysis based deep learning on round-reduced SPECK32/64, and finally reduces the remaining security of 11-round SPECK32/64 to roughly 38 bits. In this paper, we are committed to evaluating the safety of SIMON cipher under the neural differential cryptanalysis. We firstly prove theoretically that SIMON is a non-Markov cipher, which means that the results based on conventional differential cryptanalysis may be inaccurate. Then we train a residual neural network to get the 7-, 8-, 9-round neural distinguishers for SIMON32/64. To prove the effectiveness for our distinguishers, we perform the distinguishing attack and key-recovery attack against 15-round SIMON32/64. The results show that the real ciphertexts can be distinguished from random ciphertexts with a probability close to 1 only by 2^8.7 chosen-plaintext pairs. For the key-recovery attack, the correct key was recovered with a success rate of 23%, and the data complexity and computation complexity are as low as 2⁸ and 2^20.1 respectively. All the results are better than the existing literature. Furthermore, we briefly discussed the effect of different residual network structures on the training results of neural distinguishers. It is hoped that our findings will provide some reference for future research.

• Journal of Korean Association for Spatial Structures
• /
• v.22 no.2
• /
• pp.39-46
• /
• 2022

Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyper-parameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.

• ETRI Journal
• /
• v.44 no.4
• /
• pp.641-653
• /
• 2022

In the process of autonomous vehicle motion planning and to create comfort for vehicle occupants, factors that must be considered are the vehicle's safety features and the road's slipperiness and smoothness. In this paper, we build a mathematical model based on the combination of a genetic algorithm and a neural network to offer lane-change solutions of autonomous vehicles, focusing on human vehicle control skills. Traditional moving planning methods often use vehicle kinematic and dynamic constraints when creating lane-change trajectories for autonomous vehicles. When comparing this generated trajectory with a man-generated moving trajectory, however, there is in fact a significant difference. Therefore, to draw the optimal factors from the actual driver's lane-change operations, the solution in this paper builds the training data set for the moving planning process with lane change operation by humans with optimal elements. The simulation results are performed in a MATLAB simulation environment to demonstrate that the proposed solution operates effectively with optimal points such as operator maneuvers and improved comfort for passengers as well as creating a smooth and slippery lane-change trajectory.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.3
• /
• pp.125-132
• /
• 2023

Roundabouts have strengths in traffic flow and safety but can present difficulties for inexperienced drivers. Demand to acquire and analyze drone images has increased to enhance a traffic environment allowing drivers to deal with roundabouts easily. In this paper, we propose a roundabout traffic analysis system that detects, tracks, and analyzes vehicles using a deep learning-based object detection model (YOLOv7) in drone images. About 3600 images for object detection model learning and testing were extracted and labeled from 1 hour of drone video. Through training diverse conditions and evaluating the performance of object detection models, we achieved an average precision (AP) of up to 97.2%. In addition, we utilized SORT (Simple Online and Realtime Tracking) and OC-SORT (Observation-Centric SORT), a real-time object tracking algorithm, which resulted in an average MOTA (Multiple Object Tracking Accuracy) of up to 89.2%. By implementing a method for measuring roundabout entry speed, we achieved an accuracy of 94.5%.

• Smart Structures and Systems
• /
• v.30 no.4
• /
• pp.397-410
• /
• 2022

It is essential to monitor the working conditions of bolt-connected joints, which are widely used in various kinds of steel structures. The looseness of bolts may directly affect the stability and safety of the entire structure. In this study, a guided wave-based method for bolt looseness detection and localization is presented for a joint structure with multiple bolts. SH waves generated and received by a small number (two pairs) of magnetostrictive transducers were used. The bolt looseness index was proposed based on the changes in the reconstructed responses excited by the time reversal signals of the measured unit impulse responses. The damage locations and local damage severities were estimated using the damage indices from several wave propagation paths. The back propagation neural network (BPNN) technique was employed to identify the local damages. Numerical and experimental studies were conducted on a lap joint with eight bolts. The results show that the total damage severity can be successfully detected under the effect of external force and measurement noise. The local damage severity can be estimated reasonably for the experimental data using the BPNN constructed by the training patterns generated from the finite element simulations.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.2
• /
• pp.164-181
• /
• 2023

In a low speed limit environment, the speed limit tolerance of automated traffic enforcement devices is very high, which is one of the main factors for the low compliance rate. Therefore, in this study, we aimed to the improve the speed limit tolerance and to present a new standard. The effects of the operator and user errors that can cause speeding by drivers were analyzed. Based on the results of the analysis, an improvement of the tolerance was proposed by applying an error in the enforcement device and GPS speed. In addition, long-term expected safety effects such as the accident rate and severity were estimated from the operator's perspective when improving the tolerance. As a result of the estimation, the speed limit compliance rate, accident rate, and change rate of a number of severe accidents due to speed change, and pedestrian traffic accident mortality rate were all improved in all speed limit environments. The introduction of the proposed improvement is expected to improve road safety significantly.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.36 no.4
• /
• pp.18-24
• /
• 2013

A document control system (DCS), ANSIM (KAERI Advanced Nuclear Safety Information Management) was designed for the purpose of documents preparation, review, and approvement for JRTR (Jordan Research and Training Reactor) project. The ANSIM system consists of a document management, document container, project management, organization management, and EPC (Engineering, Procurement and Construction) document folder. The document container folder run after specific contents, a revision history of the design documents and drawings are issued in KAERI. The EPC document work-scope is a registry for incoming documents in ANSIM, the assignment of a manager or charger, document review, preparing and outgoing PM memorandum as attached the reviewed paper. On the other hand, KAERI is aiming another extra network server for the NRR (New Research Reactor) by the end of this year. In conclusion, it is the first, computation system of DCS that provides document form, document number, and approval line. Second, ANSIM increases the productivity of performance that can be recognized the document work-flow of oneself and all participants. Finally, a plenty of experience and knowledge of nuclear technology can be transmitted to next generation for the design, manufacturing, testing, installation, and commissioning. Though this, ANSIM is expected to allow the export of a knowledge and information system as well as a research reactor.