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Determination of k0 factors of short-lived nuclides 46mSc and 110Ag for the k0-NAA
Truong Truong Son,Ho Van Doanh,Ho Manh Dung 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.9
The k0-standardization neutron activation analysis method has successfully determined the mass fraction of elements of interest using around a hundred analytical radionuclides. However, several very short-lived nuclides with half-life less than 100 s have not been used at Dalat research reactor. One of the reasons is that the values of k0-factors of these nuclides are significantly different. Therefore, this work focused on re-determination and evaluation of k0-factors of very short-lived nuclides 110Ag (T1/2 ¼ 24 s) and 46mSc (T1/2 ¼ 18.75 s). The results of determination of the short-lived nuclides revealed that k0-factor of 110Ag is significantly difference between the existing data and the obtained results in this work. The evaluation of the k0-factors was done by using the obtained results for application of k0-NAA for NIST1566b and NIST-2711A standard reference materials
Experimental Demonstration of Sequence Recognition of Serial Memristors
Son Ngoc Truong,Khoa Van Pham,양원선,조안재,Huan Minh Vo,이미정,민경식 대한금속·재료학회 2017 ELECTRONIC MATERIALS LETTERS Vol.13 No.1
The sequence recognition is very essential in mimicking brain’s neocorticalfunction because most of input patterns to brain’s neocortex are dynamicallychanging over time, not static regardless of time. In this paper, we experimentallydemonstrate the sequence recognition for various input sequences using serialmemristors, for the first time. In this experiment, the serial memristors are used,which were fabricated with carbon fiber and aluminum film on glass substrate. Toverify the sequence recognition, we store the following 3 sequences in thefabricated serial memristors, which are ‘A’→‘B’→‘C’, ‘B’→‘A’→‘C’, and‘C’→‘B’→‘A’, respectively. By performing this experiment, it is verified theserial memristors are changed to Low Resistance State only when the inputsequence matches the stored one. When the input sequence is different from thestored one, the serial memristors remain unchanged. The simple voltagecomparator can be used to sense the output voltage to indicate whether thesequence matching happens or not. This experimental demonstration can be veryuseful to realize memristor crossbars which can process the temporal andsequential patterns in future.
Son Ngoc Truong,Kyeong-Sik Min 대한전자공학회 2014 Journal of semiconductor technology and science Vol.14 No.3
In this paper, we propose a new memristorbased crossbar array architecture, where a single memristor array and constant-term circuit are used to represent both plus-polarity and minus-polarity matrices. This is different from the previous crossbar array architecture which has two memristor arrays to represent plus-polarity and minus-polarity connection matrices, respectively. The proposed crossbar architecture is tested and verified to have the same performance with the previous crossbar architecture for applications of character recognition. For areal density, however, the proposed crossbar architecture is twice better than the previous architecture, because only single memristor array is used instead of two crossbar arrays. Moreover, the power consumption of the proposed architecture can be smaller by 48% than the previous one because the number of memristors in the proposed crossbar architecture is reduced to half compared to the previous crossbar architecture. From the high areal density and high energy efficiency, we can know that this newly proposed crossbar array architecture is very suitable to various applications of analog neuromorphic computing that demand high areal density and low energy consumption.
Truong, Son Ngoc,Van Pham, Khoa,Min, Kyeong-Sik IEEE 2018 IEEE TRANSACTIONS ON NANOTECHNOLOGY Vol. No.
<P>The spatial pooler in brain's neocortex models how the cortical neurons learn the feedforward connections and forms the cortical representation from various sensory information. To realize the spatial pooler's operation in hardware, we propose a new spatial-pooling memristor crossbar that converts the inputs from sensory organ into the sparse distributed representation (SDR) of cortical neurons, in this paper. The spatial pooling is composed of overlapping, inhibition, and learning steps. The proposed memristor crossbar can perform exactly the same functions of overlapping, inhibition, and learning with the spatial-pooling software algorithm. By converting the sensory information to SDR using the spatial-pooling memristor crossbar, we can have the following advantages for the cortical information processing: first, preserving the semantic similarity through the spatial pooling; second, changing synaptic weights continuously; third, keeping the sparsity of SDR as low as 2% like brain's neocortex; and fourth, improving the noise robustness. In this paper, these four properties of the spatial pooling in brain's neocortex have been simulated and verified in the memristor crossbar circuit. For verifying the spatial-pooling operation, the memristor crossbar has been tested for MNIST image recognition in this paper. The recognition rate is as high as 95% for the memristor crossbar with 4096 columns.</P>
Truong, Son Ngoc,Shin, SangHak,Byeon, Sang-Don,Song, JaeSang,Mo, Hyun-Sun,Min, Kyeong-Sik Springer US 2015 NANOSCALE RESEARCH LETTERS Vol.10 No.1
<P>This paper performs a comparative study on the statistical-variation tolerance between two crossbar architectures which are the complementary and twin architectures. In this comparative study, 10 greyscale images and 26 black-and-white alphabet characters are tested using the circuit simulator to compare the recognition rate with varying statistical variation and correlation parameters.</P><P>As with the simulation results of 10 greyscale image recognitions, the twin crossbar shows better recognition rate by 4 % on average than the complementary one, when the inter-array correlation = 1 and intra-array correlation = 0. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture can recognize better by 5.6 % on average than the complementary one.</P><P>Similarly, when the inter-array correlation = 1 and intra-array correlation = 0, the twin architecture can recognize 26 alphabet characters better by 4.5 % on average than the complementary one. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture is better by 6 % on average than the complementary one. By summary, we can conclude that the twin crossbar is more robust than the complementary one under the same amounts of statistical variation and correlation.</P>
Son Ngoc Truong,SangHak Shin,Sang-Don Byeon,JaeSang Song,Kyeong-Sik Min IEEE 2015 IEEE TRANSACTIONS ON NANOTECHNOLOGY Vol.14 No.6
<P>In this paper, we propose a new twin crossbar architecture of binary memristors for low-power image recognition. In the new twin crossbar, we use two identical memristor arrays instead of using the previous complementary memristor arrays of M<SUP>+</SUP> and M<SUP>-</SUP>. Thereby, we can apply the discrete cosine transform (DCT) algorithm to reduce the number of low-resistance state (LRS) cells in the two identical M<SUP>+</SUP> arrays. With the reduced number of LRS cells in two M<SUP>+</SUP> arrays, the power consumption in the crossbar can be significantly saved compared to the previous complementary crossbar that is not suitable to DCT. When the number of discarded coefficients in the DCT matrix is 56.25%, 67.19%, 76.56%, and 84.38%, the power consumption of the new twin crossbar is reduced by 51.7%, 61.3%, 69.9%, and 77.4%, respectively, compared to the previous complementary one.</P>