基于邻居决策的协同过滤推荐算法

协同过滤技术应用于个性化推荐系统中,稀疏性问题和可扩展性问题成为亟需解决的问题。针对传统方法的不足,提出一种凭借邻居数做决策的方法,比较各个待测位置的用户邻居数和项目邻居数,由数量多的一方作预测,同时对预测值判定给出一种合理而有效的度量方法。实验结果表明,该方法能够提高推荐质量。     

CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention

Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.     

ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks

N6-methyladenine (6mA) has been recognized as a key epigenetic alteration that affects a variety of biological activities. Precise prediction of 6mA modification sites is essential for understanding the logical consistency of biological activity. There are various experimental methods for identifying 6mA modification sites, but in silico prediction has emerged as a potential option due to the very high cost and labor-intensive nature of experimental procedures. Taking this into consideration, developing an efficient and accurate model for identifying N6-methyladenine is one of the top objectives in the field of bioinformatics. Therefore, we have created an in silico model for the classification of 6mA modifications in plant genomes. ENet-6mA uses three encoding methods, including one-hot, nucleotide chemical properties (NCP), and electron–ion interaction potential (EIIP), which are concatenated and fed as input to ElasticNet for feature reduction, and then the optimized features are given directly to the neural network to get classified. We used a benchmark dataset of rice for five-fold cross-validation testing and three other datasets from plant genomes for cross-species testing purposes. The results show that the model can predict the N6-methyladenine sites very well, even cross-species. Additionally, we separated the datasets into different ratios and calculated the performance using the area under the precision–recall curve (AUPRC), achieving 0.81, 0.79, and 0.50 with 1:10 (positive:negative) samples for F. vesca, R. chinensis, and A. thaliana, respectively.     

基于小波的继电保护装置电磁干扰软件防护快速算法研究

基于Mallat分解的小波变换方法能可靠地检测出电力波形中的奇异点,但不适合于实际工程应用。提出了基于S变换的改进的快速算法,即对AD采样点进行前向差分。对于正常的正弦采样信号,点与点之间的采样点值相差较小,所以差分值也较小;而对于奇异信号,在奇异点附近AD采样值有突变,所以差分值较大。因此,选择合适的差分门槛值就可以检测出信号中的奇异点。分析奇异点附近差分值的正负特性可以检测出该奇异点是正常的故障信号或是受干扰信号。最后,通过对典型波形的仿真计算和实际的电磁兼容性试验,验证了该软件抗干扰措施能显著提高数字式继电保护装置运行的稳定性。     

再谈电容器真空浸渍"单台注油"装置

介绍了电容器单台注油装置的构成及工作原理,对关键零部件作了改进,对其作用进行了分析,并说明如何安装使用。     

一种高效Cable Modem SOC软硬件协同验证平台

通过对基于龙芯一号CPU的双向有线调制解调器SOC的研究,利用软件仿真和硬件模拟相结合的方法,设计了一种软硬件协同验证平台TWCNP,可分别对EuroDOCSIS协议栈、EuroDOCSIS协议处理器、整个双向有线网络SOC等软硬件进行验证,并实现了验证和设计的统一.为了提高TWCNP的验证时效性,提出了一种基于TWCNP-OS的软硬件协同验证方法,缩短了开发周期,保证了SOC设计的可靠性.     

平行单级双倒立摆系统的建模与滑模变结构控制

从两摆间无弹簧连接的平行双倒立摆系统特定物理结构出发,结合控制目标进行合理建模与分析,验证所建模型可行性,讨论系统能控性和参数间的关系,并设计了滑模变结构控制律.该文的理论和方法对平行单级双倒立摆的进一步研究工作具有重要意义.     

基于调峰形势的联络线受电模型及其优化

探讨了基于调峰形势的联络线受电优化模型。该模型根据负荷预测数据,采用分时电价计算方法,综合考虑电网内机组的发电、联络线受电和调峰能力三者的关系,在保证电网安全的条件下,得到了经济效益优化的受电曲线,从而改变了以往受电曲线由人工凭经验制定的状况,为电网的安全经济运行提供了技术依据。