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Online social networks, especially Twitter (X), have become focal points for illicit activities, providing unique criminal investigation opportunities. This paper introduces an innovative methodology that uses social media sentiment analysis to predict criminal activities. One major challenge in sentiment analysis is the uneven distribution of sentiment classes, where traditional models often fail to accurately classify instances of the minority class due to the overwhelming presence of majority class data. To tackle this issue, we propose a model that combines a reinforcement learning (RL) algorithm with a scope loss function. The RL approach uses a reward mechanism that assigns a more significant value to correctly predicting minority class instances over majority class ones. The scope loss function ensures an optimal balance between utilizing known data and exploring new data, thus maintaining a delicate equilibrium between accuracy and generalizability. Our model employs a series of convolutional neural networks (CNNs) to extract significant features from textual content, which are then utilized for sentiment classification. We also incorporate an advanced artificial bee colony (ABC) optimization technique to refine the model's hyperparameters. The effectiveness of our approach was empirically tested using two distinct datasets: one consisting of crime incident reports from the Chicago Police Department covering the period from September 2019 to July 2024 and another comprising tweets containing crime-related terms related to Chicago. The predictive capabilities of our proposed model were benchmarked against existing models, demonstrating superior performance with accuracies of 96.411% and 94.088%, respectively. This breakthrough highlights the potential of integrating sentiment analysis with reinforcement learning to significantly enhance the predictive accuracy of crime-related activities in online social networks, offering valuable insights for law enforcement and criminal investigation applications.

Rim electromagnetic direct-driven propeller (REDP) is a propeller used in the underwater vehicle. It has many merits compared with the traditional propeller. It is difficult to utilize the sensor to get the rotor position and speed that are important to control REDP. An observer based on sliding mode method was proposed to estimate the rotor position and speed in the paper. Its stability was proved and some parameters were obtained. The influence of the switching function on the observation performance was analyzed and the sigmoid function was chosen as the switching function of the sliding mode observer. Then the observation position and speed were analyzed under different parameters to get the better observation performance. Finally, the experiment verified the feasibility of sliding mode observer.

Focusing on the energy efficiency (EE) improving problem in a densely deployed cellular network with partial conventional base stations (BSs) that cannot be frequently switched on/off, this paper jointly optimizes the switching on/off strategy and user association policy with the consideration of quality-of-service (QoS). By applying a modified switching cost model, we formulate the EE improving problem as an energy efficiency maximization problem (EEMP). To solve the EEMP efficiently, we first concentrate on the EEMP with a constant user distribution where switching cost is omitted temporarily, i.e., static- EEMP, and then the EEMP can be settled by using the results of the static-EEMP. After analyzing the hardness of solving the static-EEMP directly, several sub-optimal strategies are proposed for the static-EEMP to optimize the BSs working states and user association policy. With the results obtained from solving the static-EEMP, we propose a QoS First Maximum EE (QFMEE) scheme to solve the EEMP in two steps. In the first step, necessary BSs are switched on when the user distribution changes to ensure user QoS. In the second step, we judge the extra cost and the energy saving caused by the switching off operations to improve the network EE. Simulation results verify the effectiveness of our proposed strategies for static-EEMP and show that QFMEE can improve the network EE and reduce switching cost significantly when the switching cost occupies a considerable proportion in the total energy consumption.

Presently, condition monitoring and fault diagnostics in the three-phase voltage-source inverter (VSI) system are already considered essential to improve maintenance efficiency and increase reliability levels. This paper presents a new open-switch fault real-time diagnosis method in the VSI based on the phase currents. First, on the basis of real-time measured two phase currents, the sum of the absolute value of the normalized phase currents is calculated, whose average value is less relevant to the transient condition such as load variations and current frequency variations. Second, the current zero-cross detection method is employed to avoid the influence of current distortion caused by the transient condition. Then, the fault detection signal is built to precisely localize the faulty switches, and the real-time fast diagnosis for three-phase VSI with the open-switch fault is realized. Finally, the experiment results verify the reliability and effectiveness of the proposed method.

The cloud radio access network (C-RAN) with mobile edge computing (MEC) structure which consists of a baseband unit (BBU) pool integrating with an MEC server and several remote radio heads (RRHs) beside the mobile terminals can help users with computational resource-intensive tasks and bring extra profits to network operator at the same time. This paper presents a novel task-aware C-RAN with MEC structure and formulates a profit maximization problem by jointly optimizing offloading strategy, radio and computational resources allocation under the constraints of offloading latency, fronthaul capacity along with limited bandwidth and computational resource. To solve this NP-hard optimization problem in a distributed and efficient way, we propose a spectrum efficiency (SE)-based joint optimization for offloading and resource allocation (SJOORA) scheme which decomposes the original problem into two sub-problems. A SE-based offloading strategy is proposed with confirmed resource allocation, and on the other hand, bandwidth and computational resource allocation problem is solved by using a Lagrangian multiplier method with predetermined offloading strategy. Finally, by solving these two sub-problems iteratively, a suboptimal solution is obtained for the original problem. The simulation results show that the proposed SJOORA scheme can effectively increase the profit of network operator with relative lower complexity.