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Recently, motorcycle accidents have increased as the number of motorcycle drivers has increased. Although the head and neck are the body parts most frequently injured when a motorcycle accident occurs, there is a lack of research on the protection afforded to the neck by the safety equipment used by motorcycle drivers. This study presents an airbag system that uses artificial intelligence to prevent injury to the neck of a motorcycle driver. It uses a six-axis sensor, the MPU6050 sensor, which measures acceleration and angular velocity in real time as the user moves. The angles are obtained by using the measured acceleration and angular velocity, and the accident situation is judged by AI, which analyzes the acceleration and angle data. Because data is needed for AI to learn, data by type were collected through experiments. In this study, we compare the judgement performance of a parallel neural networks–convolutional neural network and a parallel neural network.

Injuries to the head and the neck are the most frequent in the event of motorcycle accidents. But enough research has not been done to protect the neck. This paper presents an airbag system that recognizes the accident situation with Artificial Intelligence to protect the driver's neck area from motorcycle accident situations when driving. In some papers with similar themes, most of them are judged based on a critical point. However, in the case of an accident judgment using the critical point, a malfunction may occur such that the airbag operates when a similar operation is performed, or the airbag does not operate due to failing to pass the critical point at the time of an accident. Artificial intelligence was used to avoid malfunctions and inconveniences. Artificial intelligence can solve the problem of malfunction that occurs when it is judged as a critical point and can solve the inconvenience of commercialized products. The CNN presented in this paper can solve these two problems, and the accuracy of accident judgment is as high as 95.75%. Through the MPU 6050 sensor, it operates the airbag by determining the accident situation using the Artificial Intelligence that was learned in advance through the information on acceleration and angular velocity of the driver's movements that were measured in real time. To make Artificial Intelligence learn, the data were collected by dividing several types of accidents on motorcycles. In this paper, the Artificial Intelligence made by Convolutional Neural Networks (CNN) method and the Artificial Intelligence made by Neural Networks (NN) method is compared, and it is confirmed that the performance such as Test Accuracy or Train Accuracy of CNN is better.

The traffic accident occurrence rate is increasing relative to the increase in the number of people using personal mobility device (PM). This paper proposes an airbag system with a more efficient algorithm to decide the deployment of a wearable bike airbag in case of an accident. The existing wearable airbags are operated by judging the accident situations using the thresholds of sensors. However, in this case, the judgment accuracy can drop against various motions. This study used the long short-term memory (LSTM) model using the sensor values of the inertial measurement unit (IMU) as input values to judge accident occurrences, which obtains data in real time from the three acceleration-axis and three angular velocity-axis sensors on the driver motion states and judges whether or not an accident has occurred using the obtained data. The existing neural network (NN) or convolutional neural network (CNN) model judges only the input data. This study confirmed that this model has a higher judgment accuracy than the existing NN or CNN by giving strong points even in “past information” through LSTM by regarding the driver motion as time-series data.

Given their wide applicability, vehicle positioning systems are being actively studied currently. As the conventional global positioning system (GPS) cannot determine the vehicle position inside tunnels and other GPS-shaded areas, we propose to improve vehicle positioning using a fuzzy logic system that can retrieve the position of a moving vehicle in such areas. The proposed system analyzes the chromaticity and frequency components of active LED illumination, which is being commonly installed at locations like inside tunnels. We verify the system performance in a test environment that resembles a tunnel. As the proposed method uses the chromaticity and frequency of LED lights for vehicle positioning, it is suitable for areas where GPS reception is poor or unavailable.

Due to the development of leisure sports industry and the increase in delivery demand, the demand for two-wheeled vehicles such as the motorcycle is increasing every year; moreover, the motorcycle accident rate is increasing. The motorcyclist’s body is exposed to the outside, and in cases of accidents, the head and the neck are particularly vulnerable. This paper proposes a study about an air bag equipped with Artificial Intelligence to protect the driver’s neck spine from motorcycle accidents. Through the six-axis sensor, it receives the driver’s motor condition data about the acceleration and angular velocity data and measures real time speed and angle; combines them with algorithms that can judge accidents through Artificial Intelligence learning to activate airbags in real time. Data were collected and learned by dividing the types of accidents; for Artificial Intelligence learning, the general Neural Network method was not used however, a mix of parallel Neural Network with an existing Neural Network were used instead. The Artificial Intelligence learning method proposed in this paper has been found to have more improved accuracy, stability and learning time compared to the existing Neural Network.

Drones in flight make a variety of vibration frequencies. Therefore, gimbal system is needed to get clean images from drone-mounted cameras. The system consists of two things: one is a structure to support a camera module, and another is a stabilizer to block outside vibrations and keep the right angle. The purpose of this paper focuses on the system's dynamics model and the application of a traditional PID controller. Furthermore, the paper compares the traditional PID and intelligent-PID without any dynamics model through the simulation made of MATLAB/Simulink.

In the field of electronic warfare applications, high voltage power supply(HVPS) for high power phased array transmitters must necessarily have an independent operating characteristics for driving mini TWTs. TWT independent operational characteristics, in order to run without interrupting the electronic warfare mission by maintaining the partial transmission function even when one of the TWT has occurred a failure, is known to be very important. In this paper, we describe the research on high-speed independent control system using a high-density HVPS, including FE modulators, hardware configuration and the FPGA in order to independently operate mini TWTs. Also, we have simulated some possible faults in phased array transmitters, and presents the test results to control a faulted TWT independently.

The common way to obtain a stereoscopic image of a subject at a distance is to place two cameras on the parallel axis rather than crossing axis. To find the IAD and maximum focal length, left and right images are obtained by varying the IAD of cameras and the focal length of the camera lens and the depth budget for the obtained images is analyzed through post production. Then, the database for IAD and focal length of the camera lens with the depth range that does not cause visual fatigue and visual discomfort are developed. These data are used to design fuzzy control and deduce the IAD and focal length of the camera lens to shoot a subject at a distance, and the function of the fuzzy control is confirmed through the actual shooting within the range of deduced IAD and focal length of the camera lens. 키워드 : 원거리 입체영상, 수평식 리그, 깊이범위, 깊이 총량, 퍼지추론 Key word : Remote Stereoscopic Photographs, Parallel Rig, IAD, Depth Range, Depth Budget, Fuzzy InferenceJournal of the Korea Institute of Information andCommunication Engineering

This paper proposes cross-coupling synchronous velocity control based on model reference adaptive control (MRAC) method for an uncertain model of transformer winding system with two non-symmetrical axial systems such as a winding spindle system and a nozzle feed drive system. Since it is difficult to achieve the physical parameters in modeling of the transformer winding system, MRAC is used to ensure the stability of the transformer winding system. In order to minimize the synchronous velocity error between two axial systems, a cross-coupling synchronous control is employed. Accordingly, the velocity error of the winding spindle system is reflected to the nozzle feed drive system and vice versa. Simulation and experimental results show that the proposed controller which is applied to the transformer winding system with uncertain parameters can reduce the synchronous velocity error between two axial systems.

This paper focuses on the feasibility of IEEE 802.11b wireless LAN for real-time wireless industrial network. In general, it has been known that IEEE 802.11b is not suitable for industrial networking because its medium access control method is the contention-based CSMA/CA (Carrier Sensing Multiple Access with Collision Avoidance) that exhibits unstable performance under heavy traffic and unbounded delay distribution. As an attempt to circumvent the probabilistic nature of IEEE 802.11b, this paper presents an enhanced four-layer architecture using the Network Driver Interface Specification (NDIS) and a virtual polling algorithm. Also, the enhanced four-layer architecture's performance is compared with that of conventional IEEE 802.11b. Based on the experimental results, it is found that the enhanced four-layer architecture for IEEE 802.11b is a very promising alternative for wireless industrial networking.