Advanced Capsule Networks via Context Awareness

Capsule Networks (CN) offer new architectures for Deep Learning (DL) community. Though its effectiveness has been demonstrated in MNIST and smallNORB datasets, the networks still face challenges in other datasets for images with distinct contexts. In this research, we improve the design of CN (Vector version) namely we expand more Pooling layers to filter image backgrounds and increase Reconstruction layers to make better image restoration. Additionally, we perform experiments to compare accuracy and speed of CN versus DL models. In DL models, we utilize Inception V3 and DenseNet V201 for powerful computers besides NASNet, MobileNet V1 and MobileNet V2 for small and embedded devices. We evaluate our models on a fingerspelling alphabet dataset from American Sign Language (ASL). The results show that CNs perform comparably to DL models while dramatically reducing training time. We also make a demonstration and give a link for the purpose of illustration.Comment: 12 page

Evaluation of municipal waste management options by circular prevention tools to give better ways for sustainable transition – A case study of Hanoi

The transition management approach can help to improve municipal solid waste management in individual cities and city regions. The obsolete technological solutions of waste management cannot support efficient and sustainable urban waste management processes. We would like to present a possible solution to development of the municipal solid waste management system in a high population density megapolis, Hanoi (Vietnam). We examined the development opportunities at three strategic levels (governmental, enterprise and personal levels). We have analyzed the system at strategic, tactical, operational and reflexive levels also, using a transition matrix. Five development aspects and technological directions have been identified, and all of them could be applied at the three decision levels. We came to the conclusion that intervention is needed at all three levels. Based on our results, we have made proposals for the transformation of Hanoi solid waste management structure in the overall organizational structure

An Improvement for Capsule Networks using Depthwise Separable Convolution

Capsule Networks face a critical problem in computer vision in the sense that the image background can challenge its performance, although they learn very well on training data. In this work, we propose to improve Capsule Networks' architecture by replacing the Standard Convolution with a Depthwise Separable Convolution. This new design significantly reduces the model's total parameters while increases stability and offers competitive accuracy. In addition, the proposed model on $64\times64$ pixel images outperforms standard models on $32\times32$ and $64\times64$ pixel images. Moreover, we empirically evaluate these models with Deep Learning architectures using state-of-the-art Transfer Learning networks such as Inception V3 and MobileNet V1. The results show that Capsule Networks perform equivalently against Deep Learning models. To the best of our knowledge, we believe that this is the first work on the integration of Depthwise Separable Convolution into Capsule Networks.Comment: 6 page

Study of interference fit between steel and brass parts

Interference fits are generally used in mechanical systems because they have low-cost production and their assembly parts are much smaller than other mechanical joints. Also, their geometric shapes and material properties allow technicians to actively determine how strong the fits are. In this study, let’s present research on interference fits between steel and brass assembly parts. The experimental processes were accomplished with five pairs of specimens to evaluate the behaviours of surface asperities under a high loading condition. Specifically, the specimen pair includes a C45 steel shaft and a C2680 brass hub, which have different surface roughness values (Ra). Let’s apply high-precision methods in measuring all dimensional parameters and employed axial load tests for distinctively inspecting the steel-brass interference fit performance. In every experiment, the measured responses are: 1) the surface roughness values (Ra) before and after loading cycles; 2) the axial load (Fa); 3) the relative displacement value or the real-time interface length in loading stages (l). The aim of this study is to propose a new relative interference value specifically between steel and brass assembly parts, which can help determine the interference loss value more accurately. It was not concluded that with the relative interference of 2.25 ‰ the load capability of steel-brass interference fits is extended. Besides, let’s narrow down the predictive loss coefficient (a) for steel-brass interference assemblies ranging from 1.1 to 2.1, which varies from widely used standards considering a=3. This result helps minimize inaccuracies in interference fit designs, calculations, and work capabilities

Evaluating the effect of self-interference on the performance of full-duplex two-way relaying communication with energy harvesting

In this paper, we study the throughput and outage probability (OP) of two-way relaying (TWR) communication system with energy harvesting (EH). The system model consists two source nodes and a relay node which operates in full-duplex (FD) mode. The effect of self-interference (SI) due to the FD operation on the system performance is evaluated for both one-way full duplex (OWFD) and two-way full duplex (TWFD) diagrams where the amplify-and-forward (AF) relay node collects energy harvesting with the time switching (TS) scheme. We first propose an individual OP expression for each specific source. Then, we derive the exact closed-form overall OP expression for the OWFD diagram. For the TWFD diagram, we propose an approximate closed-form expression for the overall OP. The overall OP comparison among hybrid systems (Two-Way Half-Duplex (TWHD), OWFD, TWFD) are also discussed.  Finally, the numerical/simulated results are presented for Rayleigh fading channels to demonstrate the correction of the proposed analysis

An Improved White Space Prediction Algorithm for Cognitive Radio Systems

Cognitive radio (CR) is a promising technology to enhance the current low usage of limited frequency resources. TV white space (TVWS) - TV bands at a particular time in a particular geographic area that are not being used by licensed services - is perceived as the most suitable frequency bands for CR. This paper proposes a new prediction TVWS algorithm for CR systems based on the ITU 1546.1 and the Okumura-Hata models. The proposed algorithm is verified with the data of 22 provinces in the South of Vietnam. The numerical results confirm the advantage of the proposed algorithm as well as the possibility of TVWS CR networks

Investigating the Effect of Matrices and Densities on the Efficiency of HPGe Gamma Spectroscopy Using MCNP

When determining radioactivities in environmental samples using low-level gamma spectroscopy, in order to raise detection limit, voluminous samples are used. It takes in account for the self-absorption (self-attenuation) of gamma rays in samples. The self-absorption effect is small or large depend on the sample shapes, matrices and densities. In this paper, we investigated the effect of some regular matrices such as water, soil, epoxy resin on the detector efficiency. Some analytical formulas for the correction of matrix and densities for soil sample was established and applied to calculate some activities from standard sample of IAEA-375

THIẾT LẬP CHỈ SỐ CHẤT LƯỢNG NƯỚC DỰA VÀO PHÂN TÍCH THỐNG KÊ: ÁP DỤNG CHO SÔNG HƯƠNG, TỈNH THỪA THIÊN HUẾ

Huong River’s water quality was preliminarily assessed by comparing the parameters monitored with the Vietnam Technical Regulation on Surface Water Quality. The river water quality was then assessed based on Water Quality Index (WQI). Principal Component Analysis (PCA) was applied to the river water quality data during 2017–2020 to determine the weighting (wi) of the ith water quality parameter for WQI calculation. The WQI was calculated both from wi and subindex (qi). The parameters selected (n = 11) for WQI calculation consisted of pH, EC (electric conductivity), DO, TSS, BOD5, COD, N-NH4+, N-NO3–, P-PO43–, Fe (total dissolved iron), and TC (total coliform). The parameters were monitored at 8–10 sites in 4–5 sessions (February, May, August, and November). The results show that 95% of WQI at 90–100 corresponds to water quality level ‘good’ and ‘excellent’; only 5% of WQI values at 49–77 (mainly in November 2020) corresponds to the level from ‘bad’ to ‘good’. In the flood-rainy season, the increase in concentrations of TSS and Fe and the decrease in DO concentration lead to a reduction in WQI. The river water quality was not significantly differed by space/monitoring sites (p > 0,05) with median WQIs of 97–100 but varied over time: the years 2017 and 2019 had median WQIs (99), higher than that in the years 2018 and 2020 (97) with p < 0,01.Chất lượng nước (CLN) sông Hương được đánh giá sơ bộ qua so sánh các thông số quan trắc với quy định kỹ thuật Việt Nam về CLN mặt. Tiếp theo, CLN sông được đánh giá qua Chỉ số chất lượng nước (WQI). Phương pháp phân tích thành phần chính (PCA) được áp dụng cho dữ liệu CLN sông giai đoạn 2017–2020 để xác định trọng số (wi) của thông số CLN i trong tính toán WQI. Chỉ số chất lượng nước được tính từ cả trọng số và chỉ số phụ (qi). Các thông số được lựa chọn để tính WQI gồm (n = 11): pH, EC (độ dẫn điện), DO, TSS, BOD5, COD, N-NH4+, N-NO3–, P-PO43–, Fe (tổng sắt tan) và TC (tổng coliform). Các thông số đó được quan trắc ở 8–10 vị trí trong 4–5 đợt (tháng 2, 5, 8 và 11). Kết quả cho thấy, 95% các giá trị WQI nằm trong khoảng 90–100, ứng với CLN loại ‘tốt’ và ‘rất tốt’; chỉ 5% các giá trị WQI nằm trong khoảng 49–77 (chủ yếu vào tháng 11/2020), ứng với CLN loại ‘xấu’ đến ‘tốt’. Vào mùa mưa lũ, nồng độ TSS và Fe tăng lên, nồng độ DO giảm, dẫn đến làm giảm WQI. Chất lượng nước sông không khác nhau có ý nghĩa thống kê theo không gian/vị trí quan trắc (p > 0,05) với WQI trung vị 97–100 nhưng khác nhau theo thời gian: năm 2017 và 2019 có WQI trung vị (99) lớn hơn năm 2018 và 2020 (WQI trung vị 97) với p < 0,01