A Flexible Two-Tower Model for Item Cold-Start Recommendation

One of the main challenges in recommendation system is the item cold-start problem, where absence of historical interactions or ratings in new items makes recommendation difficult. In order to solve the cold-start problem, hybrid neural network models using meta data of the item as a feature is widely used. However, existing cold-start models tend to focus too much on utilizing the side information of items, which may not be flexible enough to capture the interaction information of users. In this study, we propose a flexible framework for better capturing the interaction information of users. Specifically, we incorporate the multiple choice learning scheme into the two-tower neural network which is a popular recommendation model that consists of two towers – one for users and one for items. In our proposed framework, we construct two encoders. One of the two encoders, the tightly-coupled encoder, focuses on the side information of items with which the user has interacted, the other one, loosely-coupled encoder, focuses the user’s interaction information. We utilize Gumbel-Softmax to stochastically select the encoder, enhancing the flexibility that considers not only item feature but also user interaction information. We evaluate our proposed framework on two datasets – the MLIMDb dataset which is a combination of widely used the MovieLens and IMDb datasets based on common movies, and the CiteULike dataset. The experimental results show that our proposed framework achieves state-of-the-art results on cold-start recommendation. In the Recall@150 experiments on the CiteULike dataset, we achieved improvement of approximately 2.7% compared to the base model. In the Recall@150 experiments on the MLIMDb dataset, we achieved improvement of approximately 5.2% compared to the base model. We further show our proposed model improves the performance in the warm-start settings. In the Recall@100 experiments on the Citeulike dataset, we observed an improvement of approximately 1.3% compared to the base model. In the Recall@100 experiments on the MLIMDb dataset, we observed an improvement of approximately 3.9% compared to the base model. Our proposed framework provides a flexible approach for capturing the diverse aspects of users in recommendation systems, even for cold-start items. As demonstrated through extensive experiments, our proposed model outperforms several State-Of-The-Art (SOTA) models on both datasets.

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Design, Fabrication and Test of a High- Temperature Superconducting Linear Synchronous Motor Mover Magnet Prototype for High-Speed Maglev

High-temperature superconducting linear synchronous motors (HTS-LSMs) have many advantages, such as high thrust density, high efficiency, large electromagnetic gap, and liquid-helium-free refrigeration, because of the high operating temperature and good mechanical tolerance of high-temperature superconductors. Therefore, HTS-LSMs have broad application prospects in the field of high-speed maglev propulsion system. To study the dynamic stability of an HTS-LSM, this work aims at designing, fabricating and testing an HTS magnet as the mover magnet of an HTS-LSM. The HTS mover magnet is a monopole HTS magnet, and it is designed according to electromagnetic, structural, and thermal properties and the measurement system. A thermal model and structural dynamics model were constructed to analyze the dynamic refrigeration performance and structural dynamics characteristics of the HTS magnet. The validation of these models was verified by experimental results. The HTS coils in the HTS mover magnet were fabricated using epoxy impregnation with primary and secondary curing processes. Static tests and dynamic tests were performed to comprehensively study the characteristics of the HTS magnet. The magnet could be cooled to below 20 K and could be excited to 246 A with a certain temperature margin. An electromagnetic simulator was designed and manufactured to realize the off-line simulation of the actual operation of the HTS-LSM. The dynamic experimental results show that the HTS magnet could withstand a vibration environment of up to 18 gRMS without quenching and structural damage. This study provides useful information for the design and application of an HTS-LSM

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Power Electronics Based on Wide-Bandgap Semiconductors: Opportunities and Challenges

The expansion of the electric vehicle market is driving the request for efficient and reliable power electronic systems for electric energy conversion and processing. The efficiency, size, and cost of a power system is strongly related to the performance of power semiconductor devices, where massive industrial investments and intense research efforts are being devoted to new wide bandgap (WBG) semiconductors, such as silicon carbide (SiC) and gallium nitride (GaN). The electrical and thermal properties of SiC and GaN enable the fabrication of semiconductor power devices with performance well beyond the limits of silicon. However, a massive migration of the power electronics industry towards WBG materials can be obtained only once the corresponding fabrication technology reaches a sufficient maturity and a competitive cost. In this paper, we present a perspective of power electronics based on WBG semiconductors, from fundamental material characteristics of SiC and GaN to their potential impacts on the power semiconductor device market. Some application cases are also presented, with specific benchmarks against a corresponding implementation realized with silicon devices, focusing on both achievable performance and system cost.

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Segmental Degradation RUL Prediction of IGBT Based on Combinatorial Prediction Algorithms

Aiming at the segmentation nonlinear degradation characteristics of IGBT, the traditional single remaining useful lifetime (RUL) method has low accuracy. This paper proposes a method combining gray prediction and particle filter algorithm. The gray prediction model is used for slow degradation trends prediction in the early stage. When the health precusor parameters reach the fault warning line, the improved particle filter algorithm is used for this stage’s prediction with the characteristics of fast nonlinear degradation. The comparison analysis result shows that the combinatorial prediction algorithms used in this paper can be better for tracking the degradation trends of IGBT, and the prediction accuracy is higher than either of the two single prediction methods.

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Filterless and Compact ANy-WDM Transmission System Based on Cascaded Ring Modulators

To cope with the exponential increase in internet services and corresponding data traffic, especially data centers and access networks require new high data rate transmission methods with low cost, very small package and low energy consumption. In this paper, we demonstrate a filterless, agnostic Nyquist wavelength division multiplexing (ANy-WDM) transmission system based on cascaded ring modulators and a comb source. The single ring modulator acts as a filter, filtering one of the n WDM lines, generated by the comb. The same ring modulator modulates k time division multiplexed (TDM) channels on the single wavelength. Since each WDM channel, consisting of k time domain channels, has a rectangular bandwidth, the aggregated symbol rate of the superchannel modulated by this system corresponds to the optical bandwidth of all n WDM channels together. The approach is very simple and compact. Since no optical filters, delay lines or other special photonics or high bandwidth electronics is needed, an integration into any photonics platform is straightforward. Thus, the proposed method might enable very compact, ultra-high data rate transmission devices for future data centers and access networks.

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Output Power Prediction of a Photovoltaic Module Through Artificial Neural Network

With the increase in energy demand, renewable energy has become a need of almost every country. Solar Energy is an important constituent of it and contributes a large portion in it. Forecasting the output power of a Photovoltaic (PV) system has always been a challenging problem in the power sector from the last few decades. The output power of a PV system depends upon several environmental factors such as irradiance (G), temperature (T), humidity (H), wind speed (W), provided the tilt angle is kept constant, among which the vital role is played by irradiance. Researchers have utilized several techniques to accurately predict the output power of PV module but every method has various pros and cons. In this paper, an experimental measurement dataset of 28296 samples with all the environmental parameters mentioned above are taken as the inputs and power as its output, of a Poly-Silicon (Poly-Si) PV module, is trained through Artificial Neural Network (ANN), to predict the output power accurately. The proposed ANN contains a layer size of 15 and training algorithm used is Levenberg-Marquardt. A detailed analysis and preprocessing of the data is carried out through Pearson’s correlation method prior to training. The hyperparameters of Neural Network tuning are selected through heuristic method. The data division is done randomly with 70% dataset used for training, 15% dataset used for each validation and testing. The statistical results show that ANN accurately predicted the power output of PV module. The regression analysis values acquired are 98% and the MSE of all the three phases is 0.0604.

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A Novel Optimization Framework for High Dynamics Point-to-Point Direct Drive Motion Control System With a New Type of Surrogate Model

A new optimization framework for a high-dynamic point-to-point direct drive motion control system (HDPDMS) is proposed. The conventional system optimization approach considers all design parameters simultaneously, resulting in a high-dimensional search space and extensive computation. In contrast, the proposed framework uses a new DDM surrogate model that establishes a correlation between the key DDM characteristic parameters to decouple the whole optimization process. It begins with a system-level optimization to identify suitable driver types, motion profile design parameters, and characteristic parameters of the direct drive motors (DDMs) by the new surrogate model. Bayesian optimization then determines the DDM design parameters corresponding to the identified characteristic parameters. Once the DDM surrogate model is built, the proposed framework achieved the desired HDPDMS design in just 1 hour, saving 98.6% of computation time compared to the traditional approach. Additionally, multi-objective optimization and Gaussian process regression prediction intervals were employed to obtain a suitable training dataset and input range for the surrogate model, resulting in a 99.8% reduction in computation resources compared to the traditional DDM surrogate model. Through completing three unique motion task optimizations and creating a prototype, the optimization framework was proven effective, demonstrating the potential of this novel method.

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Smishing Strategy Dynamics and Evolving Botnet Activities in Japan

XLoader and FakeSpy, the two major smishing botnets targeting Japan, change their attack strategies over various timescales. Based on recent observations of the botnets and Twitter data, we present empirical facts about their strategies and activity patterns and applied some of these strategic and activity patterns to malware detection and malicious domain detection. All the proposed methods yielded small false positive and negative rates, and are expected to run on user devices owing to their small computational cost. Recent malware detection methods based on traffic analysis extract TCP/IP traffic features if the upper layers of TCP are encrypted. In this study, Frida’s hooking capability was employed to decode the upper layers (WebSocket and JSON-RPC) to create a list of all commands flowing over the botnet channel. The command-level traffic analysis presents decisive attack features because commands are transmitted according to strategies developed by the attackers. The proposed malicious domain detection method, on the other hand, exploited the tendency of the attackers to create domains in batches. Previous researchers focused on how benign and malicious domains were registered and used on the name servers. The proposed method, on the other hand, focuses on the arrival rate of SMS messages with URL links. The error rates become significantly small when users do not receive such messages very often.

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Intention to Adopt Industry 4.0 by Organizations in Colombia, Ecuador, Mexico, Panama, and Peru

This study aims to understand the factors that drive actors belonging to the sector of organizations in Latin America (LA) to adopt Industry 4.0. The proposed model results from the analysis and integration of the technology adoption model (TAM), green information technology adoption model (GITAM), and theory of planned behavior (TPB). To determine the predictive factors for internal organizational actors, the research team surveyed information on organizations belonging to Colombia, Ecuador, Mexico, Panama, and Peru. Information was collected from strategic, tactical, and operational personnel. Data were collected from 499 organizational actors in the productive sector, processed, and analyzed using a structural equation model with the partial least squares technique. The study model explains, first there is an influence of the variables Industry 4.0 perceived ease of use (PEU) and Industry 4.0 perceived utility (PUT) on Industry 4.0 attitude towards use (ATU). Second, there is a positive influence of Industry 4.0 technological context (ICO), Industry 4.0 subjective norm (SNO), Industry 4.0 attitude (ATT), Industry 4.0 attitude towards to use (ATU), and Industry 4.0 attitude behavioral control (BCO) on intention to adopt Industry 4.0 in the organization (IAI). Third, what was not supported is the influence of Industry 4.0 technological context (ICO) on the intention to adopt Industry 4.0 in the organization (IAI). The model results are consistent with those of other studies on technology adoption, and propose a model for Industry 4.0, which is a significant contribution to this study, especially for developing countries.

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Induced Overvoltage Caused by Indirect Lightning Strikes in Large Photovoltaic Power Plants and Effective Attenuation Techniques

Indirect Lightning Stroke (ILS) is considered an urgent issue on overall power systems due to its sudden dangerous occurrence. A grid-connected solar Photovoltaic (PV) power plant of 1MW was considered and analyzed using PSCAD/EMTDC software. The effect of grounding grid resistance ( Rg ) on the induced voltages caused by the indirect strokes was discussed. The Transient Grounding Potential Rise (TGPR) variation with Rg was presented and discussed. Four different models were proposed and installed for the system under study, which includes a combination of the Externally Gapped Line Arrester (EGLA) with the Non-Gapped Line Arrester (NGLA). The results show that when the Rg was reduced from 5 to 1 ohm, TGPR decreased by about 79.63%, whereas the peak value was reduced by about 91.3% nearby the striking position. Four models of EGLAs were proposed to reduce the induced transient overvoltage effectively. The four models showed a remarkable ability to reduce the backflow current (BFC) and, consequently, the induced overvoltage. The EGLA’s type with the composite air gap reduced the TGPR by about 77.04 % and reduced the induced overvoltage nearby the striking position by about 51.3%.

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