Cyber-Physical Systems (CPSs), especially those involving autonomy, need guarantees of their safety. Runtime Enforcement (RE) is a lightweight method to formally ensure that some specified properties are satisfied ove...
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This paper introduces PRMNet, an advanced deep model with a convolutional architecture for segmenting wireless signals in time-frequency occupancy spectrograms, designed to enhance spectrum sensing accuracy. PRMNet fe...
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The study examined the ability of a Light Gradient Boosting Machine (LightGBM) to predict stock prices with Exponential Moving Averages (EMA_5) and Simple Moving Averages (SMA_5) as primary Technical Indicators. Due t...
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ISBN:
(数字)9798331527549
ISBN:
(纸本)9798331527556
The study examined the ability of a Light Gradient Boosting Machine (LightGBM) to predict stock prices with Exponential Moving Averages (EMA_5) and Simple Moving Averages (SMA_5) as primary Technical Indicators. Due to the autocorrelation in time series data, the adjusted closing prices were considered to run the model with several indicators effectively. These adjusted prices are subsequently used to compute the EMA_5 and SMA_5 as features in the model, to smooth price predictions and provide a clearer understanding of short-term market movements by identifying emerging patterns and trends with greater precision. The performance of LightBGMs will also be evaluated against ARIMA using statistical measures, including Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicated that for most instances, LightGBM outperforms ARIMA, producing R2 scores higher than 99%. It showcases the possibility of LightGBM as a robust stock market prediction model when it comes to the apparent complexities of market data. Future work will further extend the model's features to improve its already stunning, reliable prediction accuracy.
This paper presents a simplified process for synthesizing LiNi0.5Co0.2Mn0.3O2 (NCM523) coated with Li1.3Al0.3Ti1.7(PO4)3 (LATP) to optimize electrochemical characteristics by adjusting LATP content adjustment. LATP, a...
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Turntable servo systems are important experimental equipment used for semi-physical simulation and testing of aircraft, which have very strict requirements for tracking performance. To achieve high-precision servo per...
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Channel state information (CSI) is essential to the performance optimization of intelligent reflecting surface (IRS)-aided wireless communication systems. However, the passive and frequency-flat reflection of IRS, as ...
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Channel state information (CSI) is essential to the performance optimization of intelligent reflecting surface (IRS)-aided wireless communication systems. However, the passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed practical challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To tackle the above challenge, we propose a novel neural network (NN)-empowered IRS channel estimation and passive reflection design framework for the wideband orthogonal frequency division multiplexing (OFDM) communication system based only on the user’s reference signal received power (RSRP) measurements with time-varying random IRS training reflections. As RSRP is readily accessible in existing communication systems, our proposed channel estimation method does not require additional pilot transmission in IRS-aided wideband communication systems. In particular, we show that the average received signal power over all OFDM subcarriers at the user terminal can be represented as the prediction of a single-layer NN composed of multiple subnetworks with the same structure, such that the autocorrelation matrix of the wideband IRS channel can be recovered as their weights via supervised learning. To exploit the potential sparsity of the channel autocorrelation matrix, a progressive training method is proposed by gradually increasing the number of subnetworks until a desired accuracy is achieved, thus reducing the training complexity. Based on the estimates of IRS channel autocorrelation matrix, the IRS passive reflection is then optimized to maximize the average channel power gain over all subcarriers. Numerical results indicate the effectiveness of the proposed IRS channel autocorrelation matrix estimation and passive reflection design under wideband channels, which can achieve significant performance improvement compared to the existing IRS re
We define the reduced biquaternion tensor ring (RBTR) decomposition and provide a detailed exposition of the corresponding algorithm RBTR-SVD. Leveraging RBTR decomposition, we propose a novel low-rank tensor completi...
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This paper focuses on an integrated sensing and communication (ISAC) system in the presence of signal-dependent modulated jamming (SDMJ). Our goal is to suppress jamming while carrying out simultaneous communications ...
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In computational structural biology, predicting metal-binding sites and their corresponding metal types is challenging due to the complexity of protein structures and interactions. Conventional sequence- and structure...
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This paper explores a multi-user covert communication scenario with the assistance of a reconfigurable intelligent surface (RIS). Except a covert user monitored by the warden, there are multiple public users existing ...
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