• Clean Technology
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• v.27 no.2
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• pp.182-189
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• 2021

The adsorption of disperse yellow 3 (DY 3) on granular activated carbon (GAC) was investigated for isothermal adsorption and kinetic and thermodynamic parameters by experimenting with initial concentration, contact time, temperature, and pH of the dye as adsorption parameters. In the pH change experiment, the adsorption percent of DY 3 on activated carbon was highest in the acidic region, pH 3 due to electrostatic attraction between the surface of the activated carbon with positive charge and the anion (OH^-) of DY 3. The adsorption equilibrium data of DY 3 fit the Langmuir isothermal adsorption equation best, and it was found that activated carbon can effectively remove DY 3 from the calculated separation factor (R_L). The heat of adsorption-related constant (B) from the Temkin equation did not exceed 20 J mol^-1, indicating that it is a physical adsorption process. The pseudo second order kinetic model fits well within 10.72% of the error percent in the kinetic experiments. The plots for Weber and Morris intraparticle diffusion model were divided into two straight lines. The intraparticle diffusion rate was slow because the slope of the stage 2 (intraparticle diffusion) was smaller than that of stage 1 (boundary layer diffusion). Therefore, it was confirmed that the intraparticle diffusion was rate controlling step. The free energy change of the DY 3 adsorption by activated carbon showed negative values at 298 ~ 318 K. As the temperature increased, the spontaneity increased. The enthalpy change of the adsorption reaction of DY 3 by activated carbon was 0.65 kJ mol^-1, which was an endothermic reaction, and the entropy change was 2.14 J mol^-1 K^-1.

• Applied Chemistry for Engineering
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• v.22 no.4
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• pp.358-366
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• 2011

A homologous series of linear liquid crystal dimers, the ${\alpha},{\omega}$-bis(4-cyano-azobenzene-4'-oxy)alkanes (CATWETn, where n, the number of methylene units in the spacer, is 2~10) were synthesized, and their thermotropic liquid crystalline phase behavior were investigated. The CATWETn with n of 3 and 6 exhibited monotropic nematic phases, whereas other derivatives showed enantiotropic nematic phases. The nematic-isotropic transition temperatures of the dimers and their entropy variation at the phase transition showed a large odd-even effect as a function of n. This phase transition behavior was rationalized in terms of the change in the average shape of the spacer on varying the parity of the spacer. The thermal stability and degree of order in the nematic phase and the magnitude of the odd-even effect of CATWETn were similar to those for the methoxy-, nitro-, and pentyl-substituted dimers, while they were significantly different from those for the monomesogenic compounds, 1-{4-(4'-cyanophenylazo)phenoxy}alkylbromides and the side-chain liquid-crystalline polymers, the poly[1-{4-(4'-cyanophenylazo)phenoxyalkyloxy}ethylene]s. The results were discussed in terms of 'virtual trimer model' by Imrie.

• Korean Journal of Environment and Ecology
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• v.37 no.6
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• pp.450-463
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• 2023

The objective of this study is to derive the core habitat of the Kirengeshoma koreana Nakai utilizing Habitat Suitability Index (HSI) and Maximum Entropy (MaxEnt) models. Expert-based models have been criticized for their subjective criteria, while statistical models face difficulties in on-site validation and integration of expert opinions. To address these limitations, both models were employed, and their outcomes were overlaid to derive the core habitat. Five variables were identified through a comprehensive literature review and spatial analysis based on appearance coordinates. The environmental variables encompass vegetation zone, forest type, crown density, annual precipitation, and effective soil depth. Through surveys involving six experts, importance rankings and SI (Suitability Index) scores were established for each variable, subsequently facilitating the creation of an HSI map. Using the same variables, the MaxEnt model was also executed, resulting in a corresponding map, which was merged to construct the definitive core habitat map. Out of 16 observed locations of K. koreana, 15 were situated within the identified core habitat. Furthermore, an area historically known to host K. koreana but not verified in the present, Mt. Yeongchwi, was found to lack a core habitat. These findings suggest that the developed models exhibit a high degree of accuracy and effectively reflect the current ecological landscape.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.4
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• pp.260-269
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• 2012

The aim of this study is to evaluate the applicability of adsorption models for understanding the thermodynamic properties of adsorption process. For this study, the adsorption isotherm data of $NO_3$-N ion onto a commercial anion exchange resin obtained at various experimental conditions, i.e. different initial concentrations of adsorbate, different dosages of adsorbent, and different temperatures, were used in calculating the thermodynamic parameters and the adsorption energy of adsorption process. The Gibbs free energy change (${\Delta}G^0$) of adsorption process could be calculated using the Langmuir constant $b_M$ as well as the Sips constant, even though the results were significantly dependant on the experimental conditions. The thermodynamic parameters such as standard enthalpy change (${\Delta}H^0$), standard entropy change (${\Delta}S^0$) and ${\Delta}G^0$ could be calculated by using the experimental data obtained at different temperatures, if the adsorption data well fitted to the Langmuir isotherm model and the plot of ln b versus 1/T gives a straight line. As an alternative, the empirical equilibrium constant(K) defined as $q_e/C_e$ could be used for evaluating the thermodynamic parameters instead of the Langmuir constant. The results from the applications of D-R model and Temkin model to evaluate the adsorption energy suggest that the D-R model is better than Temkin model for describing the experimental data, and the availability of Temkin model is highly limited by the experimental conditions. Although adsorption energies determined using D-R model show significantly different values depending on the experimental conditions, they were sufficient to show that the adsorption of $NO_3$-N onto anion exchange resin is an endothermic process and an ion-exchange process.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Parasites, Hosts and Diseases
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• v.30 no.2
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• pp.65-74
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• 1992

The tegumental ultrastructure of juvenile and adult Echinostoma cinetorchis (Trematoda: Echinostomatidae) was observed by scanning electron microscopy. Three-day (juvenile) and 16-day (adult) worms were harvested from rats (Sprague-Dawley) experimentally fed the metacercariae from the laboratory-infected fresh water snail, Hippeutis cantori. The worms were fifed with 2.5% glutaraldehyde, processed routinely, and observed by an ISI Korea DS-130 scanning electron microscope. The 3-day old juvenile worms were elongated and ventrally curved, with their ventral sucker near the anterior two-fifths of the body. The head crown was bearing 37∼38 collar spines arranged in a zigzag pattern. The lips of the oral and ventral suckers had 8 and 5 type II sensory papillae respectively, and bewteen the spines, a few type III papillae were observed. Tongue or spade-shape spines were distributed anteriorly to the ventral sucker, whereas peg-like spines were distributed posteriorly and became sparse toward the posterior body. The spines of the dorsal surface were similar to those of the ventral surface. The 16-day old adults were leaf-like, and their oral and ventral suckers were located very closely. Aspinous head crown, oral and ventral suckers had type II and type III sensory papillae, and numerous type I papillae were distributed on the tegument anterior to the ventral sucker. Scale-like spines, with broad base and round tip, were distributed densely on the tegument anterior to the ventral sucker but they became sparse posteriorly. At the dorsal surface, spines were observed at times only at the anterior body. The results showed that the tegument of E. cinetorchis is similar to that of other echinostomes, but differs in the number and arrangement of collar spines, shape and distribution of tegumenal spines, and type and distribution of sensory papillae.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.32-43
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• 2010

This paper presents deblocking filter for low-complexity video decoder. Baseline profile of the H.264/AVC used for mobile devices such as mobile phones has two times higher compression performance than the MPEG-4 Visual but it has a problem of serious complexity as using 1/4-pel interpolation filter, adaptive entropy model and deblocking filter. This paper presents low-complexity deblocking filter for decreasing complexity of decoder with preserving the coding efficiency of the H.264/AVC. In this paper, the proposed low-complexity deblocking filter decreased 49% of branch instruction than conventional approach as calculating value of BS by using the CBP. In addition, a range of filtering of strong filter applied in intra macroblock boundaries was limited to two pixels. According to the experimental results, the proposed low-complexity deblocking filter decreased -0.02% of the BDBitrate comparison with baseline profile of the H.264/AVC, decreased 42% of the complexity of deblocking filter, and decreased 8.96% of the complexity of decoder.

• Journal of Ecology and Environment
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• v.44 no.1
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• pp.33-40
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• 2020

Background: Understanding the geographical distribution of a species is a key component of studying its ecology, evolution, and conservation. Although Schlegel's Japanese gecko (Gekko japonicus) is widely distributed in Northeast Asia, its distribution has not been studied in detail. We predicted the present and future distribution of G. japonicus across China, Japan, and Korea based on 19 climatic and 5 environmental variables using the maximum entropy (MaxEnt) species distribution model. Results: Present time major suitable habitats for G. japonicus, having greater than 0.55 probability of presence (threshold based on the average predicted probability of the presence records), are located at coastal and inland cities of China; western, southern, and northern coasts of Kyushu and Honshu in Japan; and southern coastal cities of Korea. Japan contained 69.3% of the suitable habitats, followed by China (27.1%) and Korea (4.2%). Temperature seasonality (66.5% of permutation importance) was the most important predictor of the distribution. Future distributions according to two climate change scenarios predicted that by 2070, and overall suitable habitats would decrease compared to the present habitats by 18.4% (scenario RCP 4.5) and 10.4% (scenario RCP 8.5). In contrast to these overall trends, range expansions are expected in inland areas of China and southern parts of Korea. Conclusions: Suitable habitats predicted for G. japonicus are currently located in coastal cities of Japan, China, and Korea, as well as in isolated patches of inland China. Due to climate change, suitable habitats are expected to shrink along coastlines, particularly at the coastal-edge of climate change zones. Overall, our results provide essential distribution range information for future ecological studies of G. japonicus across its distribution range.