• Journal of Information Processing Systems
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• v.17 no.3
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• pp.462-472
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• 2021

With an increase in the scale of recommender systems, users' rating data tend to be extremely sparse. Some methods have been utilized to alleviate this problem; nevertheless, it has not been satisfactorily solved yet. Therefore, we propose an effective pre-rating method based on users' dichotomous preferences and average ratings fusion. First, based on a user-item ratings matrix, a new user-item preference matrix was constructed to analyze and model user preferences. The items were then divided into two categories based on a parameterized dynamic threshold. The missing ratings for items that the user was not interested in were directly filled with the lowest user rating; otherwise, fusion ratings were utilized to fill the missing ratings. Further, an optimized parameter λ was introduced to adjust their weights. Finally, we verified our method on a standard dataset. The experimental results show that our method can effectively reduce the prediction error and improve the recommendation quality. As for its application, our method is effective, but not complicated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1095-1109
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• 2022

Along with the popularity of GPS system and smart cell phone, trajectories of pedestrians or vehicles are recorded at any time. The great amount of works had been carried out in order to discover traffic paradigms or other regular patterns buried in the huge trajectory dataset. The core of the mining algorithm is how to evaluate the similarity, that is, the "distance", between trajectories appropriately, then the mining results will be accordance to the reality. Euclidean distance is commonly used in the lots of existed algorithms to measure the similarity, however, the trend of trajectories is usually ignored during the measurement. In this paper, a novel segment transform distance (STD) algorithm is proposed, in which a rule system of line segment transformation is established. The similarity of two-line segments is quantified by the cost of line segment transformation. Further, an improvement of STD, named ST-DTW, is advanced with the use of the traditional method dynamic time warping algorithm (DTW), accelerating the speed of calculating STD. The experimental results show that the error rate of ST-DTW algorithm is 53.97%, which is lower than that of the LCSS algorithm. Besides, all the weights of factors could be adjusted dynamically, making the algorithm suitable for various kinds of applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3043-3067
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• 2022

With the mobile traffic in the network increases exponentially, multi-access edge computing (MEC) develops rapidly. MEC servers are deployed geo-distribution, which serve many mobile terminals locally to improve users' QoE (Quality of Experience). When the cache space of a MEC server is full, how to replace the cached videos is an important problem. The problem is also called the cache replacement problem, which becomes more complex due to the dynamic video popularity and the varied video sizes. Therefore, we proposed a new cache replacement scheme based on local video popularity and video size to solve the cache replacement problem of MEC servers. First, we built a local video popularity model, which is composed of a popularity rise model and a popularity attenuation model. Furthermore, the popularity attenuation model incorporates a frequency-dependent attenuation model and a frequency-independent attenuation model. Second, we formulated a utility based on local video popularity and video size. Moreover, the weights of local video popularity and video size were quantitatively analyzed by using the information entropy. Finally, we conducted extensive simulation experiments based on the proposed scheme and some compared schemes. The simulation results showed that our proposed scheme performs better than the compared schemes in terms of hit rate, average delay, and server load under different network configurations.

• Steel and Composite Structures
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• v.47 no.6
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• pp.759-779
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• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

• Development and Reproduction
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• v.13 no.3
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• pp.183-190
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• 2009

In mammals, puberty is a dynamic transition process from infertile immature state to fertile adult state. The neuroendocrine aspect of puberty is started with functional activation of hypothalamus-pituitary-gonadal hormone axis. The timing of puberty can be altered by many factors including hormones and/or hormone-like materials, social cues and metabolic signals. For a long time, attainment of a particular body weight or percentage of body fat has been thought as crucial determinant of puberty onset. However, the precise effect of high-fat (HF) diet on the regulation of hypothalamic GnRH neuron during prepubertal period has not been fully elucidated yet. The present study was undertaken to test the effect of a HF diet on the puberty onset and hypothalamic gene expressions in immature female rats. The HF diet (45% energy from fat, HF group) was applied to female rats from weaning to around puberty onset (postnatal days, PND 22-40). Body weight and vaginal opening (VO) were checked daily during the entire feeding period. In the second experiment, all animals were sacrificed on PND 36 to measure the weights of reproductive tissues. Histological studies were performed to assess the effect of HF diet feeding on the structural alterations in the reproductive tissues. To determine the transcriptional changes of reproductive hormone-related genes in hypothalamus, total RNAs were extracted and applied to the semi-quantitative reverse transcription polymerase chain reaction (RT-PCR). Body weights of HF group animals tend to be higher than those of control animals between PND 22 and PND 31, and significant differences were observed PND 32, PND 34, PND 35 and PND 36 (p<0.05). Advanced VO was shown in the HF group (PND $32.8{\pm}0.37$ p<0.001) compared to the control (PND $38.25{\pm}0.25$). The weight of ovaries (p<0.01) and uteri (p<0.05) from HF group animals significantly increased when compared to those from control animals. Corpora lutea were observed in the ovaries from the HF group animals but not in control ovaries. Similarly, hypertrophy of luminal and glandular uterine epithelia was found only in the HF group animals. In the semi-quantitative RT-PCR studies, the transcriptional activities of KiSS-1 in HF group animals were significantly higher than those from the control animals (p<0.001). Likewise, the mRNA levels of GnRH (p<0.05) were significantly elevated in HF group animals. The present study indicated that the feeding HF diet during the post-weaning period activates the upstream modulators of gonadotropin such as GnRH and KiSS-1 in hypothalamus, resulting early onset of puberty in immature female rats.

• Macromolecular Research
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• v.12 no.6
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• pp.573-580
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• 2004

To prepare chitosan-based polymeric amphiphiles that can form nanosized core-shell structures (nanopar­ticles) in aqueous milieu, chitosan oligosaccharides (COSs) were modified chemically with hydrophobic cholesterol groups. The physicochemical properties of the hydrophobized COSs (COSCs) were investigated by using dynamic light scattering and fluorescence spectroscopy. The feasibility of applying the COSCs to biomedical applications was investigated by introducing them into a gene delivery system. The COSCs formed nanosized self-aggregates in aqueous environments. Furthermore, the physicochemical properties of the COSC nanoparticles were closely related to the molecular weights of the COSs and the number of hydrophobic groups per COS chain. The critical aggregation concentration values decreased upon increasing the hydrophobicity of the COSCs. The COSCs effi­ciently condensed plasmid DNA into nanosized ion-complexes, in contrast to the effect of the unmodified COSs. An investigation of gene condensation, performed using a gel retardation assay, revealed that $COS6(M_n=6,040 Da)$ containing $5\%$ of cholesteryl chloroformate (COS6C5) formed a stable DNA complex at a COS6C5/DNA weight ratio of 2. In contrast, COS6, the unmodified COS, failed to form a stable COS/DNA complex even at an elevated weight ratio of 8. Furthermore, the COS6C5/DNA complex enhanced the in vitro transfection efficiency on Human embryonic kidney 293 cells by over 100 and 3 times those of COS6 and poly(L-lysine), respectively. Therefore, hydrophobized chitosan oligosaccharide can be considered as an efficient gene carrier for gene delivery systems.

• Journal of Power Electronics
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• v.5 no.2
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• pp.129-141
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• 2005

A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 200F I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 200F I-PD NNMF controller. The results confirm that the proposed 2DOF I-PO NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.6
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• pp.629-636
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• 2011

It has been suggested that buildings designed for strong ground motions will also have improved resistance to air blast loads. As an initial attempt to quantify this behavior, the responses of a ten story steel building, designed for the 1994 building code, with lateral resistance provided by perimeter moment frames, is considered. An analytical model of the building is developed and the magnitude and distribution of blast loads on the structure are estimated using available computer software that is based on empirical methods. To obtain the relationship between pressure, time duration, and standoff distance, these programs are used to obtain an accurate model of the air blast loading. A hemispherical surface burst for various explosive weights and standoff distances is considered for generating the air blast loading and determining the structural response. Linear and nonlinear analyses are conducted for these loadings. Air blast demands on the structure are compared to current seismic guidelines. These studies present the displacement responses, story drifts, demand/capacity ratio and inelastic demands for this structure.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.721-726
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• 2012

In this paper, we proposed an efficient algorithm based on SPFA(shortest path faster algorithm), which is an improved the Bellman-Ford algorithm. The Bellman-Ford algorithm can be used on graphs with negative edge weights unlike Dijkstra's algorithm. And SPFA algorithm used a queue to store the nodes, to avoid redundancy, though the Bellman-Ford algorithm takes a long time to update the nodes table. In this improved algorithm, an adjacency list is also used to store each vertex of the graph, applying dynamic optimal approach. And a queue is used to store the data. The improved algorithm can find the optimal path by continuous relaxation operation to the new node. Simulations to compare the efficiencies for Dijkstra's algorithm, SPFA algorithm and improved Bellman-Ford were taken. The result shows that Dijkstra's algorithm, SPFA algorithm have almost same efficiency on the random graphs, the improved algorithm, although the improved algorithm is not desirable, on grid maps the proposed algorithm is very efficient. The proposed algorithm has reduced two-third times processing time than SPFA algorithm.

• Journal of Internet Computing and Services
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• v.6 no.1
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• pp.105-114
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• 2005

This paper describes a new measuring technique by analysing multiple time-series patterns. This paper's goal is that extracts a really measured value having a sample pattern which is the best matched with an inputted time-series, and calculates a difference ratio with the value. Therefore, the proposed technique is not a recognition but a measurement. and not a hardware but a software. The proposed technique is consisted of three stages, initialization, learning and measurement. In the initialization stage, it decides weights of all parameters using importance given by an operator. In the learning stage, it classifies sample patterns using LBG and DTW algorithm, and then creates code sequences for all the patterns. In the measurement stage, it creates a code sequence for an inputted time-series pattern, finds samples having the same code sequence by hashing, and then selects the best matched sample. Finally it outputs the really measured value with the sample and the difference ratio. For the purpose of performance evaluation, we tested on multiple time-series patterns obtained from etching machine which is a semiconductor manufacturing.