• Journal of radiological science and technology
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• v.12 no.1
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• pp.25-29
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• 1989

This paper investigated on influence on the distinguishability of renal stone in the accordance with thickness of object, x-ray tube voltage and base density. In the relationship between object and renal stone shadow, object and tube voltage, the obtained results can be summarized as the following. 1. When thickness of object was thin, the distinguishability increased in base density $2.0{\sim}2.5$, but for adults was best shown in base density 1.5. 2. In the relationship between object and tube voltage, the distinguishability increased at lower tube voltages ($50{\sim}60\;kVp$), using grid. As mentioned above, it was thought that this method was very effective in describing the phenomena of renal stone in film density 1.5, tube voltages 60 kVp.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.116-122
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• 2023

We study the relationship of entropy with the properties of molecules and also with the macroscopic specifications of the system, i.e., component and phase. Understanding different viewpoints of classical mechanics and quantum mechanics for the indistinguishability of molecules belonging to the same component, we discuss a few thermodynamic systems in which the properties of molecules are to be consistent with the component as a macroscopic term of classifying the molecules. With a clear definition of thermodynamic microstate, the drawback of the Boltzmann statistics caused by the distinguishability of molecules is avoided, and the Gibbs paradox of entropy consequently disappears. Corresponding to the characteristics of fluid and solid phases, we investigated the effects of the indistinguishability and the symmetry number of molecules and the number of microstates realized in time on the partition function and the entropy. In particular, we show that crystalline solid can be regarded as a system which does not satisfy the ergodic hypothesis.

• Bulletin of the Korean Chemical Society
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• v.18 no.7
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• pp.695-702
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• 1997

The objective of this work is to determine the sugar profiles in commercial fruit juices, and to obtain chemometric characteristics. Sugar compositions of fruit juices were determined by HPLC-RID and GC-FID via methoxymation and trimethylsilylation with BSTFA. The appearance of multiple peaks in GC analysis for carbohydrates was disadvantageous as described in earlier literatures. Fructose, glucose, and sucrose were major carbohydrates in most fruit juices. Glucose/fructose ratios obtained by GC were lower than those by HPLC. Orange juices are similar to pineapple juices in the sugar profiles. However, grape juices are characterized by its lower or no detectable sucrose content. In addition, it was also found that unsweeten juices contained considerable level of sucrose. Chemometric technique such as principal components analysis was applied to provide an overview of the distinguishability of fruit juices based on HPLC or GC data. Principal components plot showed that different fruit juices grouped into distinct cluster. Principal components analysis was very useful in fruit juices industry for many aspects such as pattern recognition, detection of adulterants, and quality evaluation.

• ETRI Journal
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• v.41 no.5
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• pp.658-669
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• 2019

For dual-channel time-frequency (TF) overlapped signals with low sparsity in underdetermined blind source separation (UBSS), this paper proposes an effective method based on interval probability to estimate and expand the types of mixing matrices. First, the detection of TF single-source points (TF-SSP) is used to improve the TF sparsity of each source. For more distinguishability, as the ratios of the coefficients from different columns of the mixing matrix are close, a local peak-detection mechanism based on interval probability (LPIP) is proposed. LPIP utilizes uniform subintervals to optimize and classify the TF coefficient ratios of the detected TF-SSP effectively in the case of a high level of TF overlap among sources and reduces the TF interference points and redundant signal features greatly to enhance the estimation accuracy. The simulation results show that under both noiseless and noisy cases, the proposed method performs better than the selected mainstream traditional methods, has good robustness, and has low algorithm complexity.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.139-144
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• 2023

Engineers prefer deep neural networks (DNNs) for solving computer vision problems. However, DNNs pose two major problems. First, neural networks require large amounts of well-labeled data for training. Second, the covariate shift problem is common in computer vision problems. Domain adaptation has been proposed to mitigate this problem. Recent work on adversarial-learning-based unsupervised domain adaptation (UDA) has explained transferability and enabled the model to learn robust features. Despite this advantage, current methods do not guarantee the distinguishability of the latent space unless they consider class-aware information of the target domain. Furthermore, source and target examples alone cannot efficiently extract domain-invariant features from the encoded spaces. To alleviate the problems of existing UDA methods, we propose the mixup regularization in adversarial discriminative domain adaptation (ADDA) method. We validated the effectiveness and generality of the proposed method by performing experiments under three adaptation scenarios: MNIST to USPS, SVHN to MNIST, and MNIST to MNIST-M.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.2
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• pp.177-184
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• 2019

Mycobacteria grow slowly. Therefore, a solid medium should be used for eight weeks and a liquid medium for six weeks. The purpose of this study was to find the growth factors that can grow Mycobacterium rapidly and to help develop a solid medium for rapid identification. Three types of Mycobacterium growth factors were evaluated with 10 Mycobacteria by adding activated charcoal, defibrinated sheep blood, and L-ascorbic acid to $Difco^{TM}$ Mycobacteria 7H11 agar (Becton, Dickinson and Company, Sparks, MD, USA). The time to detection and the distinguishability of a colony were compared with that of the current method. In the rapidly growing Mycobacterium, the difference in detection time between the new media and conventional media confirmed that the new media was faster. M. kansasii and M. intracelluare grew faster in 7H11 C than in 7H11 medium. MTB grew faster than the other media in 7H11 C. This study confirmed that the two growth factors affect fast-growing Mycobacteria and slow-growing Mycobacteria. 7H11 C showed better distinguishability than the conventional media in all 10 Mycobacterium due to the color contrast. In particular, when the MTB was grown, the size of the colonies was larger than with other media, so visualization was easy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

• Journal of Power Electronics
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• v.15 no.6
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• pp.1547-1558
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• 2015

A predictive functional control (PFC) scheme for permanent magnet synchronous motor (PMSM) servo systems is proposed in this paper. The PFC-based method is first introduced in the control design of speed loop. Since the accuracy of the PFC model is influenced by external disturbances and speed detection quantization errors of the low distinguishability optical encoder in servo systems, it is noted that the standard PFC method does not achieve satisfactory results in the presence of strong disturbances. This paper adopted the Kalman filter to observe the load torque, the rotor position and the rotor angular velocity under the condition of a limited precision encoder. The observations are then fed back into PFC model to rebuild it when considering the influence of perturbation. Therefore, an improved PFC method, called the PFC+Kalman filter method, is presented, and a high performance PMSM servo system was achieved. The validity of the proposed controller was tested via experiments. Excellent results were obtained with respect to the speed trajectory tracking, stability, and disturbance rejection.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.1
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• pp.23-29
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• 2022

This study was carried out to clarify the egg, larvae and juveniles development of Misgurnus anguillicaudatus, and relationships of M. anguillicaudatus and M. mizolepis, Cobitididae Fishes. The adult fishes were collected in Samsan-cheon, Haenam-gun, Jeollanam-do, Korea and their spawning inducement was carried by ovaprim injections. The egg shape was circular and the size was average 1.12 mm. The eggs were hatched at 61 to 72 h after fertilization. The newly hatched larvae had an average 3.23 mm in total length (TL). At 5 days after hatching, the larvae reached to post larval stage and they were 10.3 mm in TL. At 19 days after hatching, it reached to juvenile stage and was 25.3 mm in TL. The egg size of M. anguillicaudatus was almost same as M. mizolepis but the hatching period of M. anguillicaudatus has taken longer. It was possible for interspecific distinguishability of M. anguillicaudatus and M. mizolepis when their larvae reached to juvenile stage by the development of keel-like ridges.

• Journal of Communications and Networks
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• v.11 no.6
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• pp.544-555
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• 2009

This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving "air monitor" applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-$\mathbb{C}WT$) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-$\mathbb{C}WT$ features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.