Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Stock Trading using Ensemble Strategy and Deep Reinforcement Learning

The aim of this study is to create an automated trading system for trading just one stock. The use of Markov Decision Process to simulate the stock trading process (MDP). Framed as a matter of maximizing, the trading objective is expressed. Many studies are being conducted daily throughout the globe to accurately forecast share prices and assist all share market participants. The artificial neural network is mostly utilized by the prediction model. There are other additional algorithms that are used to predict stock prices. The DRL (Deep Reinforcement Learning) algorithm and reinforcement learning will be utilized in this research paper to forecast stock price and improve prediction accuracy. There are also some challenges which occur such as Data quality, Data availability and Overfitting. The major objective of this research study is to anticipate the price of stocks using artificial intelligence and algorithms like DRL (Deep Reinforcement Learning) and Reinforcement Learning. For this case study, the data from a single stock that was pulled through the Yahoo Finance API (Application Programming Interface) will be used. The data includes Open-High-Low-Close price and volume.
Panoramic 3D LiDAR-based Object Detection

Vehicle detection systems play a crucial role in preventing accidents by providing real-time information about the location and movement of vehicles on the road. Monitoring speed is also essential for safety purposes as it enables the identification of vehicles that are exceeding speed limits or driving recklessly under current road conditions. The emergence of self-driving cars, which use a combination of artificial intelligence, algorithms, and sensors to navigate roads and make decisions without human intervention, is a rapidly advancing technology. Despite its complexity, this technology offers numerous advantages such as increased safety and reduced traffic congestion. One of the most significant benefits of self-driving cars is the potential to reduce accidents caused by human error, which is the leading cause of traffic accidents. By eliminating the need for human drivers, the risk of accidents can be significantly reduced. The frequency of car accidents is alarming, with one occurring every minute according to the National Highway Traffic Safety Administration (NHTSA). Auto insurance industry statistics indicate that every driver is likely to encounter at least four car accidents in their lifetime. Inexperienced drivers, particularly those aged 16 to 20, are at a higher risk of being involved in accidents. Annually, approximately 37,000 people die in car accidents, with one fatal accident occurring every 16 minutes. Among these, nearly 8,000 deaths involve drivers aged between 16 and 20 years old. Shockingly, more than 1,600 children under the age of 15 lose their lives in car accidents each year. To combat this alarming trend, measures are being taken to promote safe driving and reduce the frequency of accidents. Additionally, self-driving cars have the potential to improve traffic flow and minimize travel time as the vehicles can communicate with each other to optimize traffic flow and avoid congestion.
RFID-Based Vehicle Localization Using Virtual Wideband Multi-Frequency Continuous Wave

Vehicular location information plays a crucial role in intelligent transportation systems. A particular focus is on developing an Internet of Things (IoT)-based sensing system for smart roads to enable high-precision vehicle localization that does not rely on global navigation satellite system (GNSS) and improve traffic safety and efficiency. As a passive low-power IoT technology, radio frequency identification (RFID) has been proposed as an alternative localization approach in GNSS-denied scenarios. However, limited by the licensed narrow bandwidth, the localization accuracy of commercial off-the-shelf (COTS) RFID readers is quite low. To achieve the RFID-based high-precision localization, we develop an RFID localization reader on a universal software radio peripheral (USRP) platform, which realizes virtual broadband multi-frequency continuous-wave (MFCW) transmission and phase extraction of the received backscattered signals. The genetic algorithm (GA) is applied to choose the optimal frequency set to minimize the range difference estimating error. By calculating the phase difference of arrival (PDOA), the localization problem can be modeled as hyperbolic equations and solved. Experimental results validate that our developed RFID-based vehicle localization system could achieve an accuracy less than 5 cm.
Live Demonstration: An Efficient Neural Network Processor with Reduced Data Transmission and On-chip Shortcut Mapping

This demonstration showcases an efficient neural network processor implemented in TSMC 28nm CMOS technology. The processor conducts neural network inference with 16-bit dynamic fix-point activation and 10-bit dynamic fix-point weight. The reconfigurable streaming architecture is employed for off-chip data transmission reduction and on-chip shortcut mapping. An integrated neural network toolchain, including network model converter, quantitative analysis tool, and deep learning compiler, is also developed for fast network deployment.
Multiple Feature Extraction Based on PPG Signal to Realize Blood Pressure Measurement of Composite Multi-channel Convolutional Neural Network Model

Photoplethysmography (PPG) changes according to arterial blood pressure (ABP). The change in the physiological signal reflects the change in the functional state of the heart and vascular system. To prevent cardiovascular disease due to hypertension, the PPG signal has been widely used for non-invasive continuous estimation of blood pressure. In recent years, artificial neural network-based algorithms have found the relationship between ABP and related physiological signals. Compared with traditional cuff measurement methods, it is more intuitive to understand the overall blood pressure trend and avoid invasive pressure Measured bleeding risk. Most artificial neural networks use an electrocardiogram (ECG) and PPG at the same time, and the ECG acquisition device needs electrodes and wires to the chest cavity, which indirectly affects the convenience of physiological signal acquisition. Using three feature extraction techniques with different detailed attributes for a single PPG measurement and substituting short-time Fourier transform (STFT), continuous wavelet transform (CWT), and Hilbert-Huang transform (HHT) into the "compound multi-channel convolutional neural network(CMCNN)" model, we evaluate the overall blood pressure value. Considering that the distorted data caused by motion artifacts causes the training results to fail to converge, we select Physionet's MIMIC-III database and use data purification-based processing steps. We screen out the artifact labels for PPG and ABP at first and then use the artifact labels to perform seven-level data segmentation. Finally, using STFT, CWT, and HHT, we obtain spectrogram, scalogram, and Hilber spectrum. The original waveform of PPG and the spectrum, the scalogram, and the Hilbert spectrum are used as input data for learning and analysis. Compare with the traditional machine learning method, the blood pressure estimation results is closer to the actual situation.
Precision Localization: An Experimental Study on BLE Fingerprinting and Trilateration with ESP32

This paper presents a method for indoor localization using two complementary techniques: Fingerprinting (FP) and Trilateration. The system is based on Bluetooth Low Energy (BLE) technology and a network of wireless anchors to determine the location of a mobile device. The FP approach collects Received Signal Strength Indicator (RSSI) data from BLE anchors in various indoor locations and creates a map of the environment in an offline stage. During the online stage, the device’s location is determined by comparing its current RSSI measurements to the map. The FP technique showed an accuracy of 79.31% without positions on the other side of a separating concrete wall and 71.88% including those positions. In contrast, trilateration calculates the position of the device using distance measurements from multiple anchors, achieving an accuracy of 44.44%. Both techniques were tested in various environments and layouts, and the results indicate that FP is more suitable for indoor localization, particularly in large buildings with many rooms. This paper highlights the potential of these two techniques for practical indoor localization applications. Moreover, one of the experimental setups for the fingerprinting technique was tested in multiple rooms separated by a concrete wall to provide information about the potential scalability of the technique.
Joint Planning of Substations and Feeders in Urban Distribution Network Considering Real Street Layouts

There is a significant gap between the academic research and practical application of urban distribution network planning. In general, the distribution network planning in academic research does not consider the impact of real street layout on planning, which makes most distribution network models difficult to apply in practice. In addition, the planning of substations and feeders in urban distribution network planning is often carried out separately, which often leads to suboptimal planning results. The objective function that is often used in academia is also difficult to meet the needs of field planners. Aiming at the above problems, this paper proposes a joint planning model of substations and feeders in urban distribution network considering real street layout, fully considers the influence of real street layout on feeders and substations planning. The model uses the minimum load moment as the objective function and further improves it. The proposed model is recast as a mixed integer linear programming (MILP) problem, and it can be solved by the state-of-art commercial solver. The effectiveness and validity of the proposed model are illustrated on a 16-block test system.
Automatic Measurement of Scoliosis Based on an Improved Segmentation Model

The detection of the spine is crucial in automating the measurement of the Cobb Angle. While various segmentation models have been employed for vertebrae segmentation in X-ray images, there is a need to enhance segmentation performance. This paper proposes a comprehensive automatic measurement method for the Cobb angle. The RetinaNet model is employed to detect the region of interest corresponding to the spine, while the W-Net model is developed for accurate vertebrae segmentation. To address the issue of adjacent vertebrae adhesion in the segmented image, a post-processing technique is applied. Experimental results demonstrate that the W-Net model achieves superior performance, with a mean Intersection over Union (MIoU) of 0.9073 ± 0.0021, Dice Coefficient of 0.9446 ± 0.0139, and Precision of 0.9390 ± 0.0190. The post-processing step reduces adhesion at one end by approximately 83.4% and adhesion at both ends by approximately 83.6%. The reliability of the proposed method is evaluated through intra-group correlation coefficients (ICC) of 0.902 and 0.915, respectively, between two observers, both exceeding 0.9. The mean absolute deviation (MAD) is 3.08° and 2.91°, respectively. Therefore, the proposed method achieves automatic detection of the Cobb angle without the need for manual cropping or additional human intervention, while maintaining good reliability.
An Energy Efficient Lighting and Mobile Controlled Scoreboard in Smart Badminton Court

Electricity is one of the most common demands of people around the world, and though it can be produced, it does not satisfy the exact demand. Especially in sports courts, they provide much light in the field, but the lights will turn on in the absence of the players in the field, which leads to the waste of electricity. To overcome this problem, electricity during unused hours need to be saved and it is very hard for humans to monitor and control. To control the electricity, a prototype is designed that will turn ON and OFF the lights based on the presence of humans and dim the lights for a particular time period when humans are present in the field but no actions are sensed using the PIR sensor. By using this prototype, the electricity can be controlled, and the wastage of electricity is reduced. One of the major difficulties on the sports court is the scoreboard system. Scoreboards play a very important role in the sports field. At present, the scoreboards in the sports court are operated manually, so by replacing the conventional analogue type scoreboard with a digital type scoreboard, the manual work is eliminated. An IOT-based scoreboard using the Raspberry Pi is designed by which the scoreboard can be controlled with the help of a mobile phone. The primary aim of the proposed system is to ensure that the score can be controlled from a computer or mobile phone with the help of the application developed for it and that the score is displayed on digital screens.
Benefit Analysis of High Proportion of Renewable Energy Participation in Electricity Market Considering Capacity Compensation

In recent years, the large-scale integration of renewable energy into the electricity market is an inevitable trend. However, it also brings out issues as the losses of generating enterprises, and the insufficient system adequacy. Therefore, it is urgent to introduce a capacity compensation mechanism in the construction of the market mechanism. In this paper, main capacity compensation mechanisms and their adaptability to China are firstly compared and summarized. Secondly, a method to determine the capacity compensation standard, based on the needs of China's primary stage of power market, is proposed. Then, a benefit analysis model of high proportion of renewable energy is established. Finally, an example is set to analyze the impact of renewable energy participation in the market. Simulation results show that a high proportion of renewable energy participating in the electricity market will effectively reduce the clearing price, promote the low-carbon transformation of energy, shift thermal power resources to flexible resources, and at the same time verify the necessity of introducing a capacity compensation mechanism.
An Improved Harmonic Detection Algorithm Combined with Blackman Window Bispectral Interpolation and Time Shift Phase Difference Method

In order to improve the analysis accuracy, anti-interference and stability of the harmonic detection algorithm, an improved harmonic detection algorithm with Blackman window bispectral interpolation combined with full-phase time-shift phase difference method is proposed. Based on the analysis of the principles of the Blackman window bispectral interpolation method and the full-phase time-shift phase difference method, the harmonic detection algorithm is combined and optimized. And the frequency measurement accuracy is improved, and the measurement stability in the case of harmonic frequency fluctuation and noise interference is verified. The results show that the algorithm has high measurement accuracy and good performance, and has high application value.
Taguchi’s design’s Optimization of Commercial Steel Welding made by Semi-Automated GTAW

GTAW is a widely used welding process in industry. It has advantageous mechanical and metallurgical properties over other welding processes such as GMAW. In order to obtain optimal welding conditions, it’s vital to optimize the welding parameters. Commercial steel also referred as mild steel is often used for construction that needs to yield tensile strength. The later was reported to be sensitive to the hardness of welding. The work is to investigate the influence or effect of the transverse tensile strength and hardness of mild steel welding made by semi-controlled GTAW at a variation of groove shape, welding current, and welding speed. The analysis made with Taguchi’s design orthogonal array technique to demonstrate the effect of the welding parameters and optimize these parameters on the tensile strength of the welding. The results showed that higher transverse tensile strength showed higher welding hardness (i.e., 232 MPa and 2555 VHN shown at V-shaped welding). The tensile strength and hardness increased at lower welding current (170 A), V-shaped welding, and higher welding speed (150 mm/min). This combination has shown optimal conditions for stronger and highly effective GTAW welding. The increased internal stresses in welding caused lower transverse tensile strength and hardness of welding. Possibly the lower interpass heat input to increase and thus, lower transverse tensile strength of welding.
Virtual Reality Based Manual Spraying Modeling and Simulation

In the process of ship painting, the distribution of coating thickness and its calculation is difficult due to the complexity of the coating formed by manual spraying. Therefore, a modeling and simulation method of manual spraying based on virtual reality (VR) is proposed. The manual spraying model (MSM) is established by the relationship between gun parameters, position, direction, and coating thickness. Then the MSM is verified by designing a spraying test. The results show that the simulation and test error is less than 8%, which proves the accuracy of MSM in this paper. Based on the MSM, a VR-based ship spraying simulation scene was developed using C# and Unity3D, and the error was less than 5% by comparing it with the coating thickness of the actual test. This further illustrates the reliability of the MSM in this paper.
Three Challenges in ReRAM-Based Process-In-Memory for Neural Network

Artificial intelligence (AI) has been successfully applied to various fields of natural science. One of the biggest challenges in AI acceleration is the performance and energy bottleneck caused by the limited capacity and bandwidth of massive data movement between memory and processing units. In the past decade, much AI accelerator work based on process-in-memory (PIM) has been studied, especially on emerging non-volatile resistive random access memory (ReRAM). In this paper, we provide a comprehensive perspective on ReRAM-based AI accelerators, including software-hardware co-design, the status of chip fabrications, researches on ReRAM non-idealities, and support for the EDA tool chain. Finally, we summarize and provide three directions for future trends: support for complex patterns of models, addressing the impact of non-idealities such as improving endurance, process perturbations, and leakage current, and addressing the lack of EDA tools.
Rolling Bearing Fault Diagnosis based on ICEEMDAN-WTATD-DaSqueezeNet

Rolling bearing is a mechanical part of industrial scenarios which is extensively used. The working environment of the bearing is complex including high load, high pressure, and high temperature, so the failure probability is high. So the vibration signals of bearings are often accompanied by a large amount of noise, leading to unstable signal data characteristics. Aiming at the characteristics of data instability and a large number of complex noises, an adaptive threshold denoising (WTAT) method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with wavelet transform is proposed. Then combined with an improved SqueezeNet model, an accurate diagnosis of rolling bearing fault was achieved. Firstly, the ICEEMDAN is used for mode decomposition to obtain a sequence of intrinsic mode functions (IMFs), which effectively solves the mode aliasing and pseudo-mode problems of traditional empirical mode decomposition. Moreover, the WTAT noise reduction is carried out for several IMFs with the least correlation, which effectively solves the problem of over-strangling and over-retention of traditional wavelet denoising. Then, in order to better integrate local features and global dependencies, the dual attention convolutional module is embedded in the fire module of SqueezeNet and a lightweight model named dasqueeezenet is built for bearing fault diagnosis. Finally, compared with common convolutional neural networks such as VGG16, ResNet and Xception, the presented method achieves remarkable results in terms of the accuracy with 96.17%, the precision with 96.3%, the recall rate with 96.42% and the F1-Score with 96.26%, and the presented model's effectiveness in diagnosing rolling bearing faults has been demonstrated.
VoxSculpt: An Open-Source Voxel Library for Tomographic Volume Sculpting in Virtual Reality

Manual processing of tomographic data volumes, such as interactive image segmentation in medicine or paleontology, is considered a time-consuming and cumbersome endeavor. Immersive volume sculpting stands as a potential solution to improve its efficiency and intuitiveness. However, current open-source software solutions do not yield the required performance and functionalities. We address this issue by contributing a novel open-source game engine voxel library that supports real-time immersive volume sculpting. Our design leverages GPU instancing, parallel computing, and a chunk-based data structure to optimize collision detection and rendering. We have implemented features that enable fast voxel interaction and improve precision. Our benchmark evaluation indicates that our implementation offers a significant improvement over the state-of-the-art and can render and modify millions of visible voxels while maintaining stable performance for real-time interaction in virtual reality.
A Lightweight Convolutional Network Combined with Channel Attention for Non-intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is the basis of end-side informatization applications in smart grids. At the same time, the fine-grained power consumption of equipment decomposed by non-intrusive load monitoring algorithms also plays an important role in adjusting the power consumption structure. At present, deep neural network has become the focus of research in the field of non-intrusive load identification, but most neural network models only focus on how to improve the identification accuracy, while ignoring the network size requirements of hardware monitoring devices in the actual deployment process. In this paper, we propose a lightweight convolutional neural network combined with channel attention mechanism (LACNet). Through the serialized multi-scale dilated convolution structure, while increasing the receptive field, reducing the parameters to achieve the purpose of compressing the model size, and also using the channel attention mechanism to optimize different features to improve the model identification accuracy. Finally, we conducted experimental verification on the public dataset UK-DALE. The results showed that LACNet outperforms several existing load identification networks in terms of EA and other evaluation metrics. At the same time, the model parameters are also greatly reduced.
Where fewer pixels give you more image

Single-pixel cameras are both elegant and intellectually appealing, but can they outperform a camera based upon a focal-plane detector array? Single-pixel cameras use a digital mirror device to apply a time varying mask to an image of the scene, and a single-pixel detector to measure the light transmission through each of these masks. Given knowledge of the mask patterns and the measured transmissions, an image of the object is calculated.
Calibration of SINS by Specific Force Differences

A method for calibration of strapdown inertial navigation systems (SINS) is proposed. It combines the advantages of methods based on the use of the Kalman filter, on the one hand, and on estimates of changes in specific forces in the horizon plane before and after rotation of the rate table, and taking into account the actual rotations and positions of the rotary rate table. The proposed method makes it possible to calibrate the SINS of the navigation accuracy class on rough rate tables, including taking into account the inaccuracy of rotation of the module of the SINS sensitive elements in shock absorbers.