• Journal of Chest Surgery
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• v.20 no.3
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• pp.603-609
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• 1987

The clinical, investigative, and surgical experiences were reviewed in four patients with congenitally corrected transposition of the great arteries who presented to the National Medical Center between August 1983 and August 1985. This condition is very rare congenital anomaly defined as the combination of atrioventricular discordance and transposition of the great arteries. Examples of primitive [single] ventricle inverted [that is, left sided in situs solitus] with outflow chamber were excluded in this paper. According to the sequential arrangement of the hearts there were two cases of [S,L,L] and two cases of [I,D,D]. The surgical approach should be focused on minimizing the risk of heart block and increasing the degree of relief of pulmonary outflow tract obstruction [POTO]. We experienced complete heart block in two cases of [S,L,L] and significant residual POTO in one case of [S,L,L] and one case of [I,D,D] postoperatively. There were one hospital mortality caused by complete heart block and residual POTO and two delayed mortalities caused by congestive heart failure and sepsis respectively.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2227-2229
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• 2004

Fault Detection and Isolation(FDI) schemes using unknown input functional observers with very low order are presented. These schemes resolve the major practical difficulties with all FDI systems employing multiple observers for residual generation and can be implemented by the use of microprocessors that are normally used in commercial processes mainly due to the simplicity of the residual generation block. Various design objectives including detection, isolation, estimation and compensation of instrument fault/or process fault are achievable with these schemes. The proposed FDI scheme is applied to an inverted pendulum control system for instrument fault detection.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.979-993
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• 2021

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). In this study, we considered the problem that ASC faces in real-world applications that the model used should have low-complexity. We compared several models that apply light-weight techniques. First, a base CNN model was proposed using log mel-spectrogram, deltas, and delta-deltas features. Second, depthwise separable convolution, linear bottleneck inverted residual block was applied to the convolutional layer, and Quantization was applied to the models to develop a low-complexity model. The model considering low-complexity was similar or slightly inferior to the performance of the base model, but the model size was significantly reduced from 503 KB to 42.76 KB.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.154-156
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• 2021

Depth-wise separable convolution 은 컴퓨터 자원이 제한된 환경에서 기존의 standard convolution을 대체하는데 강력하고, 효과적인 대안으로 잘 알려져 있다.[1] MobileNetV2 에서는 Inverted residual block을 소개한다. 이는 depth-wise separable convolution으로 인해 생기는 손실, 즉 channel 간의 데이터를 조합해 새로운 feature를 만들어낼 기회를 잃어버릴 때, 이를 depth-wise separable convolution 양단에 point-wise convolution(1×1 convolution)을 사용함으로써 극복해낸 block이다.[1] 하지만 1×1 convolution은 채널 수에 의존적(dependent)인 특징을 갖고 있고, 따라서 결국 네트워크가 깊어지면 깊어질수록 효율적이고(efficient) 가벼운(light weight) 네트워크를 만드는데 병목 현상(bottleneck)을 일으키고 만다. 이 논문에서는 channel-wise squeeze and excitation block(CSE)을 통해 1×1 convolution을 부분적으로 대체하는 방법을 통해 이 병목 현상을 해결한다.