• Asia pacific journal of information systems
• /
• v.18 no.1
• /
• pp.79-96
• /
• 2008

One of the roles of the Semantic Web services is to execute dynamic intra-organizational services including the integration and interoperation of business processes. Since different organizations design their processes differently, the retrieval of similar semantic business processes is necessary in order to support inter-organizational collaborations. Most approaches for finding services that have certain features and support certain business processes have relied on some type of logical reasoning and exact matching. This paper presents our approach of using imprecise matching for expanding results from an exact matching engine to query the OWL(Web Ontology Language) MIT Process Handbook. MIT Process Handbook is an electronic repository of best-practice business processes. The Handbook is intended to help people: (1) redesigning organizational processes, (2) inventing new processes, and (3) sharing ideas about organizational practices. In order to use the MIT Process Handbook for process retrieval experiments, we had to export it into an OWL-based format. We model the Process Handbook meta-model in OWL and export the processes in the Handbook as instances of the meta-model. Next, we need to find a sizable number of queries and their corresponding correct answers in the Process Handbook. Many previous studies devised artificial dataset composed of randomly generated numbers without real meaning and used subjective ratings for correct answers and similarity values between processes. To generate a semantic-preserving test data set, we create 20 variants for each target process that are syntactically different but semantically equivalent using mutation operators. These variants represent the correct answers of the target process. We devise diverse similarity algorithms based on values of process attributes and structures of business processes. We use simple similarity algorithms for text retrieval such as TF-IDF and Levenshtein edit distance to devise our approaches, and utilize tree edit distance measure because semantic processes are appeared to have a graph structure. Also, we design similarity algorithms considering similarity of process structure such as part process, goal, and exception. Since we can identify relationships between semantic process and its subcomponents, this information can be utilized for calculating similarities between processes. Dice's coefficient and Jaccard similarity measures are utilized to calculate portion of overlaps between processes in diverse ways. We perform retrieval experiments to compare the performance of the devised similarity algorithms. We measure the retrieval performance in terms of precision, recall and F measure? the harmonic mean of precision and recall. The tree edit distance shows the poorest performance in terms of all measures. TF-IDF and the method incorporating TF-IDF measure and Levenshtein edit distance show better performances than other devised methods. These two measures are focused on similarity between name and descriptions of process. In addition, we calculate rank correlation coefficient, Kendall's tau b, between the number of process mutations and ranking of similarity values among the mutation sets. In this experiment, similarity measures based on process structure, such as Dice's, Jaccard, and derivatives of these measures, show greater coefficient than measures based on values of process attributes. However, the Lev-TFIDF-JaccardAll measure considering process structure and attributes' values together shows reasonably better performances in these two experiments. For retrieving semantic process, we can think that it's better to consider diverse aspects of process similarity such as process structure and values of process attributes. We generate semantic process data and its dataset for retrieval experiment from MIT Process Handbook repository. We suggest imprecise query algorithms that expand retrieval results from exact matching engine such as SPARQL, and compare the retrieval performances of the similarity algorithms. For the limitations and future work, we need to perform experiments with other dataset from other domain. And, since there are many similarity values from diverse measures, we may find better ways to identify relevant processes by applying these values simultaneously.

• Journal of Korean Academy of Nursing
• /
• v.36 no.7
• /
• pp.1204-1214
• /
• 2006

Purpose: This retrospective study was done to evaluate the status of nosocomial urinary tract infections and to determine the risk factors and transmission route of causal IRPA through molecular epidemiology. Method: Two hundred ninety-nine of 423 patients admitted to the internal medicine and surgery ICU at a university hospital incity B had a positiveurine culture. Twelve of the 299 patients who had a urinary tract infection had IRPA strains. The data was collected from November 1, 2004 to January 31, 2005. The following results were obtained after the data was analyzed using percentile and UPGMA. Result: The rate of nosocomial urinary tract infections in the ICU was 10.8%. Therewere 16.8 cases of infection based on the period of hospitalization. There were 16.9 cases of infection based on the use of a foley catheter. The rate of nosocomial urinary tract infection in the ICU and urinary tract infections related to IRPA were higher in patients with the following characteristics: men, old age, admission through the emergency room, longer than seven days admission, severity of admitting causes, disturbance of consciousness, hydration less than 300cc in 24hours, a long course of antibiotics, a long period of foley catheterization and perineal care. Most of the microorganisms that caused the urinary tract infection were gram negative bacilli, among which P. aeruginosa was found in 70 patients (18.5%) and IRPA in 12 (4.0%). Among the 12 IRPA strains that were tested with PFGE, eight showed a dice coefficient higher than 80%, suggesting a genetic relationship. They were related with the period of hospitalization in the same ICU. These patients all received direct care for a urinary tract infection. Conclusion: Through these results, IRPA can be consideredas a contributing factors to urinary tract infections thus, active preventative measures are needed by the medical staff.

• Journal of Microbiology and Biotechnology
• /
• v.17 no.11
• /
• pp.1772-1781
• /
• 2007

Activated sludge was sequentially adapted to benzene, toluene, and o-xylene (BTX) to study the effects on the change of microbial community. Sludge adapted to BTX separately degraded each by various rates in the following order; toluene>o-xylene>benzene. Degradation rates were increased after exposure to repeated spikes of substrates. Eleven different kinds of sludge were prepared by the combination of BTX sequential adaptations. Clustering analyses (Jaccard, Dice, Pearson, and cosine product coefficient and dimensional analysis of MDS and PCA for DGGE patterns) revealed that acclimated sludge had different features from nonacclimated sludge and could be grouped together according to their prior treatment. Benzene- and xylene-adapted sludge communities showed similar profiles. The sludge profile was affected from the point of the final adaptation substrate regardless of the adaptation sequence followed. In the sludge adapted to 50 ppm toluene, Nitrosomonas sp. and bacterium were dominant, but these bands were not dominant in benzene and benzene after toluene adaptations. Instead, Flexibacter sp. was dominant in these cultures. Dechloromonas sp. was dominant in the culture adapted to 50 ppm benzene. Thauera sp. was the main band in the sludge adapted to 50 ppm xylene, but became vaguer as the xylene concentration was increased. Rather, Flexibacter sp. dominated in the sludge adapted to 100 ppm xylene, although not in the culture adapted to 250 ppm xylene. Two bacterial species dominated in the sludge adapted to 250 ppm xylene, and they also existed in the sludge adapted to 250 ppm xylene after toluene and benzene.

• Journal of Korea Multimedia Society
• /
• v.16 no.12
• /
• pp.1376-1384
• /
• 2013

In this paper, we propose an automaticdetection method of foreign bodies through template matching in industrial CT volume data. Our method is composed of three main steps. First,Indown-sampling data, the product region is separated from background after noise reduction and initial foreign-body candidates are extracted using mean and standard deviation of the product region. Then foreign-body candidates are extracted using K-means clustering. Second, the foreign body with different intensity of product region is detected using template matching. At this time, the template matching is performed by evaluating SSD orjoint entropy according to the size of detected foreign-body candidates. Third, to improve thedetection rate of foreign body in original volume data, final foreign bodiesare detected using percolation method. For the performance evaluation of our method, industrial CT volume data and simulation data are used. Then visual inspection and accuracy assessment are performed and processing time is measured. For accuracy assessment, density-based detection method is used as comparative method and Dice's coefficient is measured.

• Investigative Magnetic Resonance Imaging
• /
• v.24 no.3
• /
• pp.123-131
• /
• 2020

Purpose: Joint label fusion (JLF) is a popular multi-atlas-based segmentation algorithm, which compensates for dependent errors that may exist between atlases. However, in order to get good segmentation results, it is very important to set the several free parameters of the algorithm to optimal values. In this study, we first investigate the feasibility of a JLF algorithm for prostate segmentation in MR images, and then suggest the optimal set of parameters for the automatic prostate segmentation by validating the results of each parameter combination. Materials and Methods: We acquired T2-weighted prostate MR images from 20 normal heathy volunteers and did a series of cross validations for every set of parameters of JLF. In each case, the atlases were rigidly registered for the target image. Then, we calculated their voting weights for label fusion from each combination of JLF's parameters (r_pxy, r_pz, r_sxy, r_sz, β). We evaluated the segmentation performances by five validation metrics of the Prostate MR Image Segmentation challenge. Results: As the number of voxels participating in the voting weight calculation and the number of referenced atlases is increased, the overall segmentation performance is gradually improved. The JLF algorithm showed the best results for dice similarity coefficient, 0.8495 ± 0.0392; relative volume difference, 15.2353 ± 17.2350; absolute relative volume difference, 18.8710 ± 13.1546; 95% Hausdorff distance, 7.2366 ± 1.8502; and average boundary distance, 2.2107 ± 0.4972; in parameters of r_pxy = 10, r_pz = 1, r_sxy = 3, r_sz = 1, and β = 3. Conclusion: The evaluated results showed the feasibility of the JLF algorithm for automatic segmentation of prostate MRI. This empirical analysis of segmentation results by label fusion allows for the appropriate setting of parameters.

• Journal of Biomedical Engineering Research
• /
• v.41 no.5
• /
• pp.195-202
• /
• 2020

Needle detection in ultrasound images is sometimes difficult due to obstruction of fat tissues. Accurate needle detection using continuous ultrasound (CUS) images is a vital stage of treatment planning for tissue biopsy and brachytherapy. The main goal of the study is classified into two categories. First, new detection model, i.e. D-Attention Unet, is developed by combining the context information of 3D medical data and CUS images. Second, the D-Attention Unet model was compared with other models to verify its usefulness for needle detection in continuous ultrasound images. The continuous needle images taken with ultrasonic waves were converted into still images for dataset to evaluate the performance of the D-Attention Unet. The dataset was used for training and testing. Based on the results, the proposed D-Attention Unet model showed the better performance than other 3 models (Unet, D-Unet and Attention Unet), with Dice Similarity Coefficient (DSC), Recall and Precision at 71.9%, 70.6% and 73.7%, respectively. In conclusion, the D-Attention Unet model provides accurate needle detection for US-guided biopsy or brachytherapy, facilitating the clinical workflow. Especially, this kind of research is enthusiastically being performed on how to add image processing techniques to learning techniques. Thus, the proposed method is applied in this manner, it will be more effective technique than before.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.36 no.6
• /
• pp.654-660
• /
• 2003

To evaluate the biological diversity of fish pathogenic streptococci, 35 strains isolated from diseased olive flounder (Paralichtys olivaceus), were analyzed using a random amplified polymorphic DNA (RAPD) technique with the oligonucleotide commercial primer 6 (Amersham Biosciences). Api 20 Strep test, drug resistance and artificial infection were carried out for further characterization of the isolates. RAPD fingerprints showed similar pattern in 25 strains (about $71.4\%$ of 35 isolates) and these strains were designed as RA group 1. Similarities greater than $44\%$ were obtained when the Dice coefficient was applied among the isolates of RA 1. On the other hand, the reference Streptococcus iniae showed a similar RAPD profile to the isolates with similarity levels of $40-93.3\%.$ Rh I was suggested to be the dominant group isolated from olive flounder suffering from streptococcosis. However, the isolates of Rh 1 group were not classified into the same species by the Api 20 Strep identification system. There was no peculiarity in drug resistance patterns of Rh I group isolates against 7 antibacterial agents. However, only 3 of 25 isolates $(0.12\%)$ showed oxytetracycline (OTC) resistance and OTC might be a useful chemotherapeutic agent in controlling the streptococcosis by strains of RA I group in olive flounder. Fish injected intraperitoneally with $10^5$ CFU of an isolate of Rh I and RA III group showed $60\%\;and\;50\%$ accumulative mortality for 20 days, respectively ($20\%$ in control or Rh II). However luther comparative studies about differences in virulence between isolates are needed.

• International Journal of Industrial Entomology and Biomaterials
• /
• v.29 no.2
• /
• pp.169-178
• /
• 2014

In this study, we investigated genetic diversity of 22 microsporidian isolates infecting tropical tasar silkworm, Antheraea mylitta collected from various geographical forest locations in the state of Jharkhand, India, using polymerase chain reaction (PCR)-based marker assay: random amplified polymorphic DNA (RAPD). A type species, NIK-1s_mys was used as control for comparison. The shape of mature microsporidians was found to be oval to elongate, measuring 3.80 to $5.10{\mu}m$ in length and 2.56 to $3.30{\mu}m$ in width. Of the 20 RAPD primers screened, 16 primers generated reproducible profiles with 298 polymorphic fragments displaying high degree of polymorphism (97%). A total of 14 RAPD primers produced 45 unique putative genetic markers, which were used to differentiate the microsporidians. Calculation of genetic distance coefficients based on dice coefficient method and clustering with un-weighted pair group method using arithmetic average (UPGMA) analysis was conducted to unravel the genetic diversity of microsporidians infecting tasar silkworm. The similarity coefficients varied from 0.059 to 0.980. UPGMA analysis generated a dendrogram with four microsporidian groups, which appear to be different from each other as well as from NIK-1s_mys. Two-dimensional distribution based on Euclidean distance matrix also revealed considerable variability among different microsporidians identified from the tasar silkworms. Clustering of few microsporidian isolates was in accordance with the geographic origin. The results indicate that the RAPD profiles and specific/unique genetic markers can be used for differentiating as well as to identify different microsporidians with considerable accuracy.

• Journal of radiological science and technology
• /
• v.45 no.2
• /
• pp.151-158
• /
• 2022

The Cardiac Gated Blood Pool (GBP) scintigram, a nuclear medicine imaging, calculates the left ventricular Ejection Fraction (EF) by segmenting the left ventricle from the heart. However, in order to accurately segment the substructure of the heart, specialized knowledge of cardiac anatomy is required, and depending on the expert's processing, there may be a problem in which the left ventricular EF is calculated differently. In this study, using the DeepLabV3 architecture, GBP images were trained on 93 training data with a ResNet-50 backbone. Afterwards, the trained model was applied to 23 separate test sets of GBP to evaluate the reproducibility of the region of interest and left ventricular EF. Pixel accuracy, dice coefficient, and IoU for the region of interest were 99.32±0.20, 94.65±1.45, 89.89±2.62(%) at the diastolic phase, and 99.26±0.34, 90.16±4.19, and 82.33±6.69(%) at the systolic phase, respectively. Left ventricular EF was calculated to be an average of 60.37±7.32% in the ROI set by humans and 58.68±7.22% in the ROI set by the deep learning segmentation model. (p<0.05) The automated segmentation method using deep learning presented in this study similarly predicts the average human-set ROI and left ventricular EF when a random GBP image is an input. If the automatic segmentation method is developed and applied to the functional examination method that needs to set ROI in the field of cardiac scintigram in nuclear medicine in the future, it is expected to greatly contribute to improving the efficiency and accuracy of processing and analysis by nuclear medicine specialists.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.3
• /
• pp.529-536
• /
• 2022

In the past two years, Severe Acute Respiratory Syndrome Coronavirus-2(SARS-CoV-2) has been hitting more and more to people. This paper proposes a novel U-Net Convolutional Neural Network to classify and segment COVID-19 lung CT images, which contains Sub Coding Block (SCB), Atrous Spatial Pyramid Pooling(ASPP) and Attention Gate(AG). Three different models such as FCN, U-Net and U-Net-SCB are designed to compare the proposed model and the best optimizer and atrous rate are chosen for the proposed model. The simulation results show that the proposed U-Net-MMFE has the best Dice segmentation coefficient of 94.79% for the COVID-19 CT scan digital image dataset compared with other segmentation models when atrous rate is 12 and the optimizer is Adam.