994 research outputs found
An evaluation methodology for the level of service at the airport landside system
A methodology is proposed for evaluating the level of service within an airport
landside system from the passenger's point of view using linguistic service
criteria. The new concept of level of service for a transport system, particularly
within the airports indicates that there must be strong stimulation in order to
proceed with the current stereotyped service standards which are being
criticised due to their being based on, either physical capacity/volume or
temporal/spatial standards that directly incorporates the perception of
passengers, the dominant users. Most service evaluation methodologies have
been concentrated on the factors of the time spent and the space provided.
These quantitative factors are reasonably simple to measure but represent a
narrow approach. Qualitative service level attributes are definitely important
factors when evaluating the level of service from a user's point of view. This
study has adopted three main evaluation factors: temporal or spatial factors as
quantitative measurements and comfort factors and reasonable service factors
as qualitative measurements. The service level evaluation involves the
passenger's subjective judgement as a perception for service provision. To
evaluate the level of service in the airport landside system from the user's
perception, this research proposes to apply a multi-decision model using fuzzy
set theory, in particular fuzzy approximate reasoning. Fuzzy set theory provides a
strict mathematical framework for vague conceptual phenomena and a
modelling language for real situations. The multi-decision model was applied to
a case study at Kimpo International Airport in Seoul, Korea. Results are
presented in terms of passenger satisfaction and dissatisfaction with a variety of
different values
Decomposed Temporal Dynamic CNN: Efficient Time-Adaptive Network for Text-Independent Speaker Verification Explained with Speaker Activation Map
Temporal dynamic models for text-independent speaker verification extract
consistent speaker information regardless of phonemes by using temporal dynamic
CNN (TDY-CNN) in which kernels adapt to each time bin. However, TDY-CNN shows
limitations that the model is too large and does not guarantee the diversity of
adaptive kernels. To address these limitations, we propose decomposed temporal
dynamic CNN (DTDY-CNN) that makes adaptive kernel by combining static kernel
and dynamic residual based on matrix decomposition. The baseline model using
DTDY-CNN maintained speaker verification performance while reducing the number
of model parameters by 35% compared to the model using TDY-CNN. In addition,
detailed behaviors of temporal dynamic models on extraction of speaker
information was explained using speaker activation maps (SAM) modified from
gradient-weighted class activation mapping (Grad-CAM). In DTDY-CNN, the static
kernel activates voiced features of utterances, and the dynamic residual
activates unvoiced high-frequency features of phonemes. DTDY-CNN effectively
extracts speaker information from not only formant frequencies and harmonics
but also detailed unvoiced phonemes' information, thus explaining its
outstanding performance on text-independent speaker verification.Comment: Submitted to InterSpeech 202
Thermal-Infrared Remote Target Detection System for Maritime Rescue based on Data Augmentation with 3D Synthetic Data
This paper proposes a thermal-infrared (TIR) remote target detection system
for maritime rescue using deep learning and data augmentation. We established a
self-collected TIR dataset consisting of multiple scenes imitating human rescue
situations using a TIR camera (FLIR). Additionally, to address dataset scarcity
and improve model robustness, a synthetic dataset from a 3D game (ARMA3) to
augment the data is further collected. However, a significant domain gap exists
between synthetic TIR and real TIR images. Hence, a proper domain adaptation
algorithm is essential to overcome the gap. Therefore, we suggest a domain
adaptation algorithm in a target-background separated manner from 3D
game-to-real, based on a generative model, to address this issue. Furthermore,
a segmentation network with fixed-weight kernels at the head is proposed to
improve the signal-to-noise ratio (SNR) and provide weak attention, as remote
TIR targets inherently suffer from unclear boundaries. Experiment results
reveal that the network trained on augmented data consisting of translated
synthetic and real TIR data outperforms that trained on only real TIR data by a
large margin. Furthermore, the proposed segmentation model surpasses the
performance of state-of-the-art segmentation methods.Comment: 12 page
Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection
2D convolution is widely used in sound event detection (SED) to recognize 2D
patterns of sound events in time-frequency domain. However, 2D convolution
enforces translation-invariance on sound events along both time and frequency
axis while sound events exhibit frequency-dependent patterns. In order to
improve physical inconsistency in 2D convolution on SED, we propose frequency
dynamic convolution which applies kernel that adapts to frequency components of
input. Frequency dynamic convolution outperforms the baseline model by 6.3% in
DESED dataset in terms of polyphonic sound detection score (PSDS). It also
significantly outperforms dynamic convolution and temporal dynamic convolution
on SED. In addition, by comparing class-wise F1 scores of baseline model and
frequency dynamic convolution, we showed that frequency dynamic convolution is
especially more effective for detection of non-stationary sound events. From
this result, we verified that frequency dynamic convolution is superior in
recognizing frequency-dependent patterns as non-stationary sound events show
more intricate time-frequency patterns.Comment: Submitted to INTERSPEECH 202
Automatic Internal Stray Light Calibration of AMCW Coaxial Scanning LiDAR Using GMM and PSO
In this paper, an automatic calibration algorithm is proposed to reduce the
depth error caused by internal stray light in amplitude-modulated continuous
wave (AMCW) coaxial scanning light detection and ranging (LiDAR). Assuming that
the internal stray light generated in the process of emitting laser is static,
the amplitude and phase delay of internal stray light are estimated using the
Gaussian mixture model (GMM) and particle swarm optimization (PSO).
Specifically, the pixel positions in a raw signal amplitude map of calibration
checkboard are segmented by GMM with two clusters considering the dark and
bright image pattern. The loss function is then defined as L1-norm of
difference between mean depths of two amplitude-segmented clusters. To avoid
overfitting at a specific distance in PSO process, the calibration check board
is actually measured at multiple distances and the average of corresponding L1
loss functions is chosen as the actual loss. Such loss is minimized by PSO to
find the two optimal target parameters: the amplitude and phase delay of
internal stray light. According to the validation of the proposed algorithm,
the original loss is reduced from tens of centimeters to 3.2 mm when the
measured distances of the calibration checkboard are between 1 m and 4 m. This
accurate calibration performance is also maintained in geometrically complex
measured scene. The proposed internal stray light calibration algorithm in this
paper can be used for any type of AMCW coaxial scanning LiDAR regardless of its
optical characteristics
Highly precise AMCW time-of-flight scanning sensor based on digital-parallel demodulation
In this paper, a novel amplitude-modulated continuous wave (AMCW)
time-of-flight (ToF) scanning sensor based on digital-parallel demodulation is
proposed and demonstrated in the aspect of distance measurement precision.
Since digital-parallel demodulation utilizes a high-amplitude demodulation
signal with zero-offset, the proposed sensor platform can maintain extremely
high demodulation contrast. Meanwhile, as all cross correlated samples are
calculated in parallel and in extremely short integration time, the proposed
sensor platform can utilize a 2D laser scanning structure with a single photo
detector, maintaining a moderate frame rate. This optical structure can
increase the received optical SNR and remove the crosstalk of image pixel
array. Based on these measurement properties, the proposed AMCW ToF scanning
sensor shows highly precise 3D depth measurement performance. In this study,
this precise measurement performance is explained in detail. Additionally, the
actual measurement performance of the proposed sensor platform is
experimentally validated under various conditions
Mediastinal lymphoma in a young Turkish Angora cat
An 8-month old intact male Turkish Angora cat was referred to the Veterinary Medical Teaching Hospital (VMTH), Seoul National University, for an evaluation of anorexia and severe dyspnea. The thoracic radiographs revealed significant pleural effusion. A cytology evaluation of the pleural fluid strongly suggested a lymphoma containing variable sized lymphocytes with frequent mitotic figures and prominent nucleoli. The feline leukemia virus and feline immunodeficiency virus tests were negative. The cat was euthanized at his owner's request and a necropsy was performed. A mass was detected on the mediastinum and lung lobes. A histopathology evaluation confirmed the mass to be a lymphoma. Immunohistochemistry revealed the mass to be CD3 positive. In conclusion, the cat was diagnosed as a T-cell mediastinal lymphoma
Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models
Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms
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