In this paper, an extended finite element method and a curve-fitting procedure are incorporated into an implicit level set method to construct a topology optimization approach inspired for manufacturing fields. The extended finite element method plays an essential role in dealing with a weak discontinuous model after utilizing a zero level set to be divided into the solid and void areas in each element. A Hamilton–Jacobi partial differential equation is transformed into ordinary differential equations over the entire design domain and updated by an upwind scheme. The proposed method is implemented in the minimum compliance design framework that has been extensively studied in topology optimization. Numerical examples illustrate the success of the presented approach in the accuracy, convergence speed and insensitivity to initial designs. It is suggested that the introduction of the nodal density variables interpolation with a level set and smoothed curve-fitting approach can be promising in material topology optimization.
International outbound call management presents significant challenges for multinational corporations due to the need to establish reliable and cost-efficient connections with numerous countries worldwide. The complexity of this task is heightened by the fact that different providers offer varying call prices and quality levels, making optimization and efficient management a daunting task for support teams. Efficient call routing is crucial for economic savings, avoiding poor call quality, and improving the overall Quality of Service (QoS) for users. In this paper, we propose a system that leverages data mining techniques to collect and analyze call logs primarily from the perspectives of costs and quality. The system is designed to develop a Smart Call Routing mechanism that optimizes routing decisions based on real-time data. With the consideration toward Multi-Objective Optimization (MOO) or Pareto optimization, we introduce a set of rules to evaluate and select the optimal routing path, focusing on maximizing cost efficiency while maintaining high call quality. This approach allows companies to optimize their call routing strategies, leading to significant cost savings, improved call quality, and proactive issue management. By minimizing potential business losses, our solution not only enhances operational efficiency but also contributes to a superior user experience.
Predicting stock prices is challenging due to the market’s inherent volatility and complexity. This paper explores how sequential models like Recurrent Neural Networks, Long Short-Term Memory, and Gated Recurrent Units can be used for stock price forecasting. To enhance prediction accuracy, Wavelet transform techniques are applied to clean the data from noise. Additionally, a dual-stage attention mechanism is introduced. This mechanism helps in capturing long-term dependencies and focusing on the most relevant data for prediction. In the initial stage, the input attention mechanism determines the most significant features at each time step by referencing the previous encoder hidden state. In the subsequent stage, the temporal attention mechanism identifies the most pertinent hidden states from across all time steps. This two-stage attention approach enhances both the accuracy of predictions and the interpretability of the model. The models are tested on various stock indices, demonstrating that combining data denoising with attention mechanisms significantly enhances forecasting accuracy.
A wireless sensor network (WSN) is a significant technological element of the Internet of Things (IoT). The core of a WSN consists of wireless sensor nodes (IoT nodes) that collect and transmit data from the installation sites to digital platforms. As IoT applications expand into various environments, the demand for low-power IoT nodes is becoming increasingly important. In this paper, we propose the schematic and implementation of low-power IoT nodes using power-saving techniques on the low-cost ESP8266 module. The IoT node contains the ESP8266 module and sensors to collect environmental data, including temperature and humidity then transmit via the ESP-NOW protocol. The ESP8266 module fully integrates both a Wi-Fi transceiver and a microcontroller, which contains central processing unit (CPU), system clock (SYC), and real-time clock (RTC) on a compact chip. However, in active mode, both the Wi-Fi transceiver and microcontroller are enabled, so the power consumption of the ESP8266 module is comparatively elevated, even when the Wi-Fi transceiver is in a state of waiting to receive or transmit data. To reduce power consumption, power-saving techniques of applying the sleep mode of the ESP8266 module was proposed in the design. Three sleep modes, including modem-sleep, light-sleep, and deep-sleep are provided by the ESP8266 module. Each sleep mode configures components such as the Wi-Fi transceiver, CPU, SYC, and RTC to be on or off differently. The IoT node was also programmed with the function of switching between sleep modes, from which users can select and configure the sleep mode and sleep duration accordingly. The power consumption of the IoT nodes is analyzed and evaluated between three sleep modes based on sleep duration and events. These IoT nodes are deployed in real-world environments to monitor various environmental conditions.
Facial Expression Recognition (FER) is a critical research area with wide-ranging applications. While advanced convolutional neural networks have enhanced FER performance, their complexity impedes deployment on resource-constrained devices. Inspired by the self-distillation architecture, this study introduces ResNet18-Lite, featuring a multi-branch architecture with Distillation Blocks designed in its shallow branchs. This novel design not only improves overall performance on FER tasks but also allows flexible branch selection during prediction, making it an optimal solution for resource-limited FER applications. Experimental results on popular FER datasets, including FER2013 and FER-Plus, demonstrate ResNet18-Lite’s superior performance compared to baseline deep learning models and other recent ResNet18 variants with Self-Distillation. Notably, ResNet18-Lite’s lightest classifier branch, utilizing only 0.9 million parameters, outperforms other more complex models.
