• Proceedings of the KSR Conference
• /
• 2011.10a
• /
• pp.670-677
• /
• 2011

In this research, noise of the Korean high-speed train running at different speed from 150 to 300 km/h was measured by using microphones and the difference of sound pressure according to its driving velocities was analyzed. In addition, sound pressure characteristics of front, pantograph, bogies and between-car sections of the Korean high-speed trains were deduced by applying delay and sum beam-forming method using a microphone array. From the research, the effects of locations of the high-speed train were investigated. The results of this research can be utilized to reduce noise of a high-speed train.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.7 no.9
• /
• pp.158-164
• /
• 1999

In this paper a simple measurement technique for mechanical-acoustic transfer functions is proposed . The mechanical-acoustic transfer functions, generally , are measured through mechanical excitations ; impact hammers or shakers. Recently , by virtue of vibro-acoustical reciprocity principle, they are measured through acoustic excitations : loudspeakers. This kind of test needs to measure the volume velocity , the radiation characteristics of a sound source. Because the volume velocity of the sound source is changed by driving signal , it is difficult to measure it. However , the new method in this paper needs not to measure the volume velocity of a sound source by combination of mechanical and acoustic excitations. Moreover, this method has the methodological advantages, such as usage of a general loudspeaker for the reciprocal excitation, no sptatial limitations for measurements of mechanical-acoustic transfer functions.

• Journal of Mechanical Science and Technology
• /
• v.20 no.1
• /
• pp.29-41
• /
• 2006

Nowadays, with the advancement of computers, computer simulation linked with VR (Virtual Reality) technology has become a useful method for designing the automotive driving system. In this paper, the VR simulation system was developed to investigate the driving performances of the ASV (Advanced Safety Vehicle) equipped with an ACC (Adaptive Cruise Control) system. For this purpose, VR environment which generates visual and sound information of the vehicle, road, facilities, and terrain was organized for the realistic driving situation. Mathematical models of vehicle dynamic analysis, which includes the ACC algorithm, have been constructed for computer simulation. The ACC algorithm modulates the throttle and the brake functions of vehicles to regulate their speeds so that the vehicles can keep proper spacing. Also, the real-time simulation algorithm synchronizes vehicle dynamics simulation with VR rendering. With the developed VR simulation system, several scenarios are applied to evaluate the adaptive cruise controlled vehicle for various driving situations.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.4
• /
• pp.142-148
• /
• 2023

In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver's state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2012.04a
• /
• pp.777-778
• /
• 2012

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2010.05a
• /
• pp.117-118
• /
• 2010

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2003.10a
• /
• pp.137-142
• /
• 2003

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer, Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method far a detection of machine malfunction or fault diagnosis.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.13 no.5
• /
• pp.81-86
• /
• 2004

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method for a detection of machine malfuction or fault diagnosis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.05a
• /
• pp.445-448
• /
• 2001

Noise radiated from pile driving operation is one of major sources of community noise pollution and thus its operation method is strictly restricted by regulations. Although the drilling method is now used been commonly used in urban areas because of its activity, the benefit of low noise decreases due to high working cost. In the present work, noise characteristics of pile driving operation are carried out. Based on the study result, a low-noise pile cap for driven piles is developed in order to satisfy both the noise level restriction and the economical efficiency. Effects of pile cap are investigated by a scale model experiment, which is focused on the variation of impact force and sound pressure level. The results show a good possibility of noise reduction by an appropriately designed pile cap.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.14 no.3
• /
• pp.143-150
• /
• 2019

In this paper, we have developed a module that prevents a driver from using mobile phone while driving. The module is equipped with a vibration sensor to determine if the vehicle is running, a BLE module to transmit the status of the module and a speaker to transmit audible/unaudible sound to the mobile terminal where GUI application is installed. If the vehicle is judged to be moving, the application is activated and the touch function of the mobile phone is blocked. Other data are also transmitted depending on the state of the module, which informs forced module removal, battery failure, or sleep mode of the module.