Optimization of technological parameters when polishing sic materials by magnetic compound fluid with the straight electromagnetic yoke

Crystallized silicon carbide (SiC) wafers are widely used in the field of integrated circuits as well as essential in the epitaxial growth of graphene and are one of the promising materials for applications in electronics at future high capacity. The surface quality of the required ultra-fine crystalline silicon wafer is the most essential factor in achieving graphene's desired electronic properties. Aiming to produce superfine surface quality SiC wafers, in this study, a new algorithm is developed to solve optimization problems with many nonlinear factors in ultra-precision machining by magnetic liquid mixture. The presented algorithm is a collective global search inspired by artificial intelligence based on the coordination of nonlinear systems occurring in machining processes. A new algorithm based on the optimization collaborative of multiple nonlinear systems (OCMNO) with the same flexibility and high convergence was established in optimizing surface quality when polishing the SiC wafers. To show the effectiveness of the proposed OCMNO algorithm, the benchmark functions were analyzed together with the established SiC wafers polishing optimization process. To give the best-machined surface quality, polishing experiments were set to find the optimal technological parameters based on a new algorithm and straight electromagnetic yoke polishing method. From the analysis and experimental results when polishing SiC wafers in an electromagnetic yoke field when using a magnetic compound fluid (MCF) with technological parameters according to the OCMNO algorithm for ultra-smooth surface quality with Ra=2.306 nm. The study aims to provide an excellent reference value in optimizing surface polishing SiC wafers, semiconductor materials, and optical device

Optimization of network traffic anomaly detection using machine learning

In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms

Payload motion control for a varying length flexible gantry crane

Cranes play a very important role in transporting heavy loads in various industries. However, because of its natural swinging characteristics, the control of crane needs to be considered carefully. This paper presents a control approach to a flexible cable crane system in consideration of both rope length varying and system constraints. At first, from Hamilton\u27s extended principle the equations of motion that characterized coupled transverse-transverse motions with varying rope length of the gantry are obtained. The equations of motion consist of a system of partial differential equations. Then, a barrier Lyapunov function is used to derive the control located at the trolley end that can precisely position the gantry payload and minimize vibrations. The designed control is verified through extensive experimental studies

On the Interference Alignment Designs for Secure Multiuser MIMO Systems

In this paper, we propose two secure multiuser multiple-input multiple-output transmission approaches based on interference alignment (IA) in the presence of an eavesdropper. To deal with the information leakage to the eavesdropper as well as the interference signals from undesired transmitters (Txs) at desired receivers (Rxs), our approaches aim to design the transmit precoding and receive subspace matrices to minimize both the total inter-main-link interference and the wiretapped signals (WSs). The first proposed IA scheme focuses on aligning the WSs into proper subspaces while the second one imposes a new structure on the precoding matrices to force the WSs to zero. When the channel state information is perfectly known at all Txs, in each proposed IA scheme, the precoding matrices at Txs and the receive subspaces at Rxs or the eavesdropper are alternatively selected to minimize the cost function of an convex optimization problem for every iteration. We provide the feasible conditions and the proofs of convergence for both IA approaches. The simulation results indicate that our two IA approaches outperform the conventional IA algorithm in terms of average secrecy sum rate.Comment: Updated version, updated author list, accepted to be appear in IEICE Transaction

Experiment-based Comparative Analysis of Nonlinear Speed Control Methods for Induction Motors

Field-oriented control (FOC) for induction motors is widely used in industrial applications. By using a fast and accurate torque controller based on a stator current controller it is possible to flexibly implement advanced speed control methods to achieve proper performance both in transient and steady-state states. In this study, a deadbeat controller was used for the current loop. The nonlinear methods used for the outer loop controller were backstepping, flatness-based control, and exact feedback linearization with state derivative. The dynamic responses of these three controls were compared through various experimental results. The advantages and disadvantages of the different control structures were analyzed and evaluated in detail. Based on this evaluation, an appropriate scheme can be specified when deployed in practice

Interregional Input-Output Analysis between the Mekong Delta Region (MDR) and the Rest of Vietnam (ROV)

The Mekong Delta is an important economic area, located in the southern part of Vietnam. The Mekong Delta has many potential and opportunities for development, but also new challenges in the context of global climate change, sea level rise, as well as the consequences of blocking the river and the Mekong countries also need to increase competition in international integration. In addition to these challenges, the region also has new opportunities when implementing economic restructuring in line with the policy of restructuring the economy in new conditions, including the establishment of special economic zones as PhuQuoc Resort. Besides analysis based on modern economic theory, this paper uses the input-output framework (I/O Inter-sect oral Scope Model) updated in 2016 for two areas: by the Mekong River and the Rest of Vietnam (ROV) to find inter-regional impacts and to calculate some impact assessments of climate change. The study also analyzes some other factors related to the viewpoint of sustainable regional development in new conditions, income distribution and social security

How heterogeneous are the determinants of total factor productivity in manufacturing sectors? Panel-data evidence from vietnam

One of the remaining challenges in explaining differences in total factor productivity is heterogeneity between sectors and within a specific sector in terms of labor and capital. This paper employs the generalized method of moments (GMM) to identify factors that affect total factor productivity across 21 manufacturing sectors and to clarify the heterogeneous determinants of total factor productivity within manufacturing sectors for the period 2010–2015. Our estimations show that large firms have significantly greater total factor productivity levels than small firms in some fragmentations of firms in terms of both labor and total capital and in some manufacturing sectors. It is suggested that firm characteristics should be considered by the government in establishing relevant policies for enhancing firm productivity

Isolation and identification of phenolic compounds from the leaf extract of Cassia alata L.

Cassia alata is one of the most important species of the genus Cassia which is rich in anthraquinones and polyphenols. This plant is used as a medicinal material of which the leaves are known to have laxative and antibiotic properties. In our study, the methanol leaf extract of C. alata showed a significant antibacterial activity against human pathogenic bacteria strains Staphylococcus aureus and Bacillus cereus. The organic layers such as n-hexane, ethyl acetate, and aqueous layers, were prepared by partitioning the methanol extract with n-hexane and ethyl acetate successively.  We successfully isolated and identified the structures of five compounds from C. alata leaves. Their structures were elucidated by MS and NMR spectroscopic methods as well as comparison with literature data. These compounds were determined to be methyl 2,4,6-trihydroxybenzoate (1), kaempferol (2), (-)epiafzelechin (3), kaempferol-3-O-glucoside (4) and kaempferol-3-O-gentiobioside (5). Keywords. Cassia alata L., epiafzelechin, kaempferol, kaempferol-3-O-glucoside, kaempferol-3-O-gentiobioside

Criblage virtuel sur grille de composés isolés au Vietnam

Criblage virtuel sur grille de composés isolés au Vietna

A Cosine Similarity-based Method for Out-of-Distribution Detection

The ability to detect OOD data is a crucial aspect of practical machine learning applications. In this work, we show that cosine similarity between the test feature and the typical ID feature is a good indicator of OOD data. We propose Class Typical Matching (CTM), a post hoc OOD detection algorithm that uses a cosine similarity scoring function. Extensive experiments on multiple benchmarks show that CTM outperforms existing post hoc OOD detection methods.Comment: Accepted paper at ICML 2023 Workshop on Spurious Correlations, Invariance, and Stability. 10 pages (4 main + appendix