• Journal of the Korean Chemical Society
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• v.54 no.4
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• pp.395-401
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• 2010

A series of macrocyclic complexes of the type [M(L/L')$(NO_3)_n$].$mH_2O$ and [VO(L/L')($SO_4$)].$2H_2O$, where L/L' is a Schiff base "3,4,10,11-tetraphenyl/tetramethyl-1,2,5,6,8,9,12,13-octaaza cyclotetradeca-2,4,9,11-tetraene-7,14-dithione" derived from thiocarbohydrazide (TCH), benzilmonohydrazone (BMH)/diacetylmonohydrazone (DMH) and carbon disulphide, M = $UO_2$ (VI), Th(IV) and ZrO(IV), n = 2, 4, m = 2, 3, have been synthesized via metal ion template methods. The complexes are characterized on the basis of elemental analysis, thermal analysis, molar conductivity, magnetic moment, electronic, infrared and $^1H$-NMR spectral studies. The ESR and cyclic voltammetry studies of the vanadyl complexes have been carried out. The results indicate that the VO(IV) ion is penta-coordinated yielding paramagnetic complexes; $UO_2$(VI) and ZrO(IV) ions are hexacoordinated where as Th(IV) ion is octa-coordinated yielding diamagnetic complexes of above composition.

• Journal of the Korean Chemical Society
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• v.53 no.6
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• pp.683-692
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• 2009

The synthesis of a novel series of nickel(II) complexes with new ligands derived from hydrazones of isoniazid have been reported in present work. The complexes have general compositions [$Ni(L)_2X_2$] or $[Ni(L)_3](ClO_4)_2$ {L = N-isonicotinamido-furfuraldimine (INH-FFL), N-isonicotinamido-3',4',5'-trimethoxybenzaldimine (INH-TMB) or N-isonicotinamido-cinnamalidene (INH-CIN) and X = $Cl^-$, ${NO_3}^-$, $ NCS^-$ or $CH_3COO^-$}. The ligands hydrazones behave as neutral bidentates (N and O donor) through the carbonyl oxygen and azomethine nitrogen. The new complexes with octahedral geometry have been characterized by elemental analysis, molecular weight determinations, magnetic susceptibility/moment, thermogravimetric, electrochemical and spectroscopic studies viz. infrared and electronic spectra. On the basis of conductivity measurements in nitrobenzene ($PhNO_2$) solution the [$Ni(L)_2X_2$] and $[Ni(L)_3](ClO_4)_2$ complexes have been found to be non-electrolytes and 1:2 electrolytes, respectively. Thermal properties have also been investigated, which support the geometry of the complexes. Antibacterial and antifungal properties of nickel(II) complexes and few standard drugs have also been examined and it has been observed that the complexes have moderate antibacterial activities.

• Journal of the Korean Magnetics Society
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• v.3 no.1
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• pp.48-55
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• 1993

We report the photoemission (PES) studies for the Co/Pd multilayter. The Co 3d PES spectrum of Co/Pd exhibits two interesting features, one near the Fermi energy, $E_{F}$, and another at ~2.5 eV below $E_{F}$. The Co 3d peak near $E_{F}$ of Co/Pd is much narrower than that of the bulk Co, consistent with the enhanced Co magnetic moment in Co/Pd compared to that in the bulk Co. The Co 3d feature at ~-2.5 eV resembles the Pd valence band structures, which suggests a substantial hybridization between the Co and Pd sublayers. The Co 3d PES spectrum of Co/Pd is compared with the existing band structures, obtained using the local spin density functional calculations. A reasonable agreement is found concerning the bandwidth of the occupied part of the Co 3d band, whereas a narrow Co 3d peak near $E_{F}$ seems not to be described by the band structure calculations.

• 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 Nutrition and Health
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• v.49 no.6
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• pp.401-410
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• 2016

Purpose: Water is magnetically charged upon contact with a magnet. Although magnetic water products have been promoted since the 1930's, they have not received wide acceptance since their effectiveness is still in question; however, some have reported their therapeutic effects on the body, especially the digestive, nervous, and urinary systems. Methods: In this study, the effect of magnetized water on glycemic control of 14 diabetic mice (CB57BK/KsJ-db/db) in comparison with 10 control mice (CB57BK/KsJ-db/+(db/+)) was investigated. Seven diabetic control (DMC) mice and seven diabetic mice + magnetized water (DM+MW) were kept for 16 weeks, followed by intraperitoneal glucose tolerance test (IPGTT). Weekly blood glucose was measured from tail veins. Blood obtained from heart puncture was used for HbA1c analysis. Results: Blood glucose level showed a significant difference starting from the $10^{th}$ week of study ($496.1{\pm}10.2mg/dl$ in DMC vs. $437.9{\pm}76.9mg/dl$ in DM+MW). Blood glucose followed by IPGTT showed no significant difference between groups at 0, 30, 60, 90, and 120 min, although glucose level at 180 min was significantly reduced in DM+MW mice. Plasma insulin level in DM+MW groups was only 39.5% of that of DMC groups ($5.97{\pm}1.69ng/ml$ in DMC vs. $2.36{\pm}0.94ng/ml$ in DM+MW). Levels of HbA1c were 12.4% and 9.7% in DMC and DM+MW groups, respectively. Conclusion: These results show the promising therapeutic effect of magnetized water in regulating blood glucose homeostasis; however, long-term supplementation or mechanistic study is necessary.