• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.35-56
• /
• 2023

With the development of information and communication technology, numerous reviews are continuously posted on websites, which causes information overload problems. Therefore, users face difficulty in exploring reviews for their decision-making. To solve such a problem, many studies on review helpfulness prediction have been actively conducted to provide users with helpful and reliable reviews. Existing studies predict review helpfulness mainly based on the features included in the review. However, such studies disable providing the reason why predicted reviews are helpful. Therefore, this study aims to propose a methodology for applying eXplainable Artificial Intelligence (XAI) techniques in review helpfulness prediction to address such a limitation. This study uses restaurant reviews collected from Yelp.com to compare the prediction performance of six models widely used in previous studies. Next, we propose an explainable review helpfulness prediction model by applying the XAI technique to the model with the best prediction performance. Therefore, the methodology proposed in this study can recommend helpful reviews in the user's purchasing decision-making process and provide the interpretation of why such predicted reviews are helpful.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.2A
• /
• pp.169-176
• /
• 2010

Generally, poly-urea is widely used as waterproof coating material due to its superior adhesiveness, elongation capacity, and permeability resistance. In addition, it can be quickly and easily applied on structure surfaces using spray application. Since it hardens in about 30 seconds after application, its construction efficiency is very high and its usage as a special functional material is also excellent. However, currently, poly-urea is mostly used as waterproof coating material and the researches on its usage as a retrofitting material is lacking at best. Therefore, basic studies on the use of poly-urea as a general structural retrofitting material are needed urgently. The objective of this study is to develop most optimum poly-urea composition for structure retrofitting purpose. Moreover, the structural strengthening capacity of the developed poly-urea is evaluated through flexural capacity experiments on RC beams and RC slabs. From the results of the flexural test of poly-urea strengthened RC beam and slab specimens, the poly-urea and concrete specimen showed monolithic behavior where ductility and ultimate strength of the poly-urea strengthened specimen showed slight increase. However, the doubly reinforced specimens with FRP sheet and poly-urea showed lower capacity than that of the specimen reinforced only with FRP sheet.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.11
• /
• pp.29-42
• /
• 2023

In this paper, we propose a method to enhance the prediction accuracy of solar irradiance for three major South Korean cities: Seoul, Busan, and Incheon. Our method entails the development of five generative models-vanilla GAN, CTGAN, Copula GAN, WGANGP, and TVAE-to generate independent variables that mimic the patterns of existing training data. To mitigate the bias in model training, we derive values for the dependent variables using random forests and deep neural networks, enriching the training datasets. These datasets are integrated with existing data to form comprehensive solar irradiance prediction models. The experimentation revealed that the augmented datasets led to significantly improved model performance compared to those trained solely on the original data. Specifically, CTGAN showed outstanding results due to its sophisticated mechanism for handling the intricacies of multivariate data relationships, ensuring that the generated data are diverse and closely aligned with the real-world variability of solar irradiance. The proposed method is expected to address the issue of data scarcity by augmenting the training data with high-quality synthetic data, thereby contributing to the operation of solar power systems for sustainable development.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.12
• /
• pp.519-524
• /
• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• The Journal of the Acoustical Society of Korea
• /
• v.26 no.6
• /
• pp.250-258
• /
• 2007

Current speech recognition technology s achieved high performance with the development of hardware devices, however it is insufficient for some applications where high reliability is required, such as voice control of powered wheelchairs for disabled persons. For the system which aims to operate powered wheelchairs safely by voice in real environment, we need to consider that non-voice commands such as user s coughing, breathing, and spark-like mechanical noise should be rejected and the wheelchair system need to recognize the speech commands affected by disability, which contains specific pronunciation speed and frequency. In this paper, we propose non-voice rejection method to perform voice/non-voice classification using both YIN based fundamental frequency(F0) extraction and reliability in preprocessing. We adopted a multi-template dictionary and acoustic modeling based speaker adaptation to cope with the pronunciation variation of inarticulately uttered speech. From the recognition tests conducted with the data collected in real environment, proposed YIN based fundamental extraction showed recall-precision rate of 95.1% better than that of 62% by cepstrum based method. Recognition test by a new system applied with multi-template dictionary and MAP adaptation also showed much higher accuracy of 99.5% than that of 78.6% by baseline system.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.2
• /
• pp.112-118
• /
• 2009

This paper proposes a rapid detection algorithm of a salient region for music video browsing system, which can be applied to mobile device and digital video recorder (DVR). The input music video is decomposed into the music and video tracks. For the music track, the music highlight including musical chorus is detected based on structure analysis using energy-based peak position detection. Using the emotional models generated by SVM-AdaBoost learning algorithm, the music signal of the music videos is classified into one of the predefined emotional classes of the music automatically. For the video track, the face scene including the singer or actor/actress is detected based on a boosted cascade of simple features. Finally, the salient region is generated based on the alignment of boundaries of the music highlight and the visual face scene. First, the users select their favorite music videos from various music videos in the mobile devices or DVR with the information of a music video's emotion and thereafter they can browse the salient region with a length of 30-seconds using the proposed algorithm quickly. A mean opinion score (MOS) test with a database of 200 music videos is conducted to compare the detected salient region with the predefined manual part. The MOS test results show that the detected salient region using the proposed method performed much better than the predefined manual part without audiovisual processing.

• Journal of the Korean earth science society
• /
• v.45 no.2
• /
• pp.111-120
• /
• 2024

Cloud coverage is a key factor in determining whether to proceed with observations. In the past, human judgment played an important role in weather evaluation for observations. However, the development of remote and robotic observation has diminished the role of human judgment. Moreover, it is not easy to evaluate weather conditions automatically because of the diverse cloud shapes and their rapid movement. In this paper, we present the development of a cloud monitoring program by applying a machine learning-based Python module "cloudynight" on all-sky camera images obtained at Miryang Arirang Astronomical Observatory (MAAO). The machine learning model was built by training 39,996 subregions divided from 1,212 images with altitude/azimuth angles and extracting 16 feature spaces. For our training model, the F1-score from the validation samples was 0.97, indicating good performance in identifying clouds in the all-sky image. As a result, this program calculates "Cloudiness" as the ratio of the number of total subregions to the number of subregions predicted to be covered by clouds. In the robotic observation, we set a policy that allows the telescope system to halt the observation when the "Cloudiness" exceeds 0.6 during the last 30 minutes. Following this policy, we found that there were no improper halts in the telescope system due to incorrect program decisions. We expect that robotic observation with the 0.7 m telescope at MAAO can be successfully operated using the cloud monitoring program.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.2
• /
• pp.139-145
• /
• 2024

This paper presents the design of a voltage controlled oscillator (VCO) which is one of the key building blocks in modern wireless communication systems with small VCO gain (K_vco) variation. To compensate conventional large K_vco variation, a series varactor bank has been added to the conventional LC-tank with parallel capacitor bank array. And also, in order to achieve excellent phase noise performance while maintaining wide tuning range, a mixed coarse/fine tuning scheme(series varactor array and parallel capacitor array) is chosen. The switched varactor array bank is controlled by the same digital code for switched capacitor array without additional digital circuits. For use at a low voltage of 1.2V, the proposed current reference circuit in this paper used a current reference circuit for safety with the common gate removed more safely. Implemented in a TSMC 0.13㎛ CMOS RF technology, the proposed VCO can be tuned from 4.4GH to 5.3GHz with the K_vco (VCO gain ) variation of less than 9.6%. While consuming 3.1mA from a 1.2V supply, the VCO has -120dBc/Hz phase noise at 1MHz offset from the carrier of the 5.3 GHz.

• Journal of Korean Society of Environmental Engineers
• /
• v.33 no.2
• /
• pp.77-84
• /
• 2011

This study was conducted to develop a novel combined system of a hybrid rotating biological contactor (RBC) process that was composed of an attached- and suspended- biomass reactor, followed by a settler and a biological aerated filter (BAF) column to treat a high strength slaughter wastewater. Long term influences of organic and nitrogen loading rates were investigated to see how the combined system worked in terms of the removal efficiency. A synthetic wastewater containing a pork cutlet steak source (commercially available) and swine blood was used to feed the combined system. The hybrid RBC process showed excellent removals: about 95% for soluble COD and 85% for ammonium nitrogen. However, the unsettled solids seriously deteriorated the removal efficiency of total COD (TCOD) and total nitrogen (TN) in the RBC process. A significant fraction of the TCOD and suspended solids (SS) was further removed in the BAF column although the effluent quality was still unsatisfactory, giving TCOD 300 mg/L, SS 180 mg/L and TN 59 mg/L. An addition of polyaluminium chloride into the RBC effluent improved the performance of the settler and BAF, producing an excellent quality of final effluent; TCOD 16.5 mg/L, SS 0 mg/L, TN 55.5 mg/L, TP 1.3 mg/L. Therefore, it was confirmed that the combined system of hybrid RBC and BAF could treat a high strength slaughter wastewater excellently.

• Journal of Korean Society of Environmental Engineers
• /
• v.27 no.2
• /
• pp.163-169
• /
• 2005

Photocatalytic oxidation of Cu(II)-EDTA has been studied using solar/$TiO_2$ photocatalysis as an energy source. Photocatalysis efficiency on the treatment of Cu(II)-EDTA was investigated using different types of solar collectors as well as by variation of the angles of solar collector solar light intensities, flow rates, and areas of solar collector. effect of $H_2O_2$ and types of $TiO_2$ catalyst on the treatment of Cu(II)-EDTA was also investigated. Removal of Cu(II) and DOC was favorable with a hemispherical collector than with a flat collector Removal of Cu(II) and DOC increased with increasing angles of solar collector up to $38^{\circ}$. Slurry type $TiO_2$ showed four-times higher removal efficiency than immobilized type $TiO_2$. Removal of both Cu(II) and DOC at a clear sky of solar light intensity ranging from 0.372 to $2.265\;mW/cm^2$ was greater than removal at a cloudy day of solar light intensity ranging from 0.038 to $1.129\;mW/cm^2$. From the result of this research that the removal efficiency of Cu(II) and DOC increased as the solar light intensity increased, it can be inferred that quantum yield in the destruction of Cu(II)-EDTA may directly related with the solar light intensity. Removal of Cu(II) increased as increasing the area of solar collector and was similar at lower flow rates white removal of Cu(II) was interfered at higher flow rates. When immobilized $TiO_2$ was used, removal efficiency of Cu(II) increased in the presence of $H_2O_2$ while negligible effect was found in the use of $TiO_2$ slurry.