Response of Hydrodynamics and Water-quality Conditions to Climate Change in a Shallow Lake

Lake water resources operation and water quality management come up with higher challenges due to climate change. The frequency and intensity of extreme hydrological events are increasing under global warming, which may directly lead to more uncertainty and complexity for hydrodynamic and water-quality conditions in large shallow lake. However, studies about effects of climate change on lake hydrodynamic and water-quality conditions are not enough. Thus, a coupled model is es-tablished to investigate the potential responses of lake water level, flow field and pollutant migra-tion to the changing climatic factors. The results imply that water flow capacity and self-purification in the Hongze Lake can be improved by west, northwest, north, south and southeast winds indi-cating wind filed change has a great effect on the hydrodynamic and water-quality conditions in large shallow lake. It is further observed that both hydrodynamics and water quality are more sensitive to rainfall change than to temperature change; compared to the effect from temperature and rainfall, the effect from wind field appear to be more pronounced. Moreover, the results verify the feasibility of coupling basin hydrological model with lake hydrodynamic and water quality model. To the best of knowledge, the coupled model should not be used until independent calibra-tions and verifications for hydrodynamics and water quality modeling, the hydrological model and the coupled model.

     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

Double Medical Images Zero-Watermarking Algorithm Based on the Chaotic System and Ternary Accurate Polar Complex Exponential Transform

Journal of Mathematical Imaging and Vision - Different from the traditional watermarking schemes, zero-watermarking schemes are lossless embedding methods, which are applicable to be used in...     

A conditional classification recurrent RBM for improved series mid-term forecasting

In the context of human-robot and robot-robot interactions, the better cooperation can be achieved by predicting the other party’s subsequent actions based on the current action of the other party. The time duration for adjustment is not sufficient provided by short term forecasting models to robots. A longer duration can by achieved by mid-term forecasting. But the mid-term forecasting models introduce the previous errors into the follow-up forecasting and amplified gradually, eventually invalidating the forecasting. A new mid-term forecasting with error suppression based on restricted Boltzmann machine(RBM) is proposed in this paper. The proposed model can suppress the error amplification by replacing the previous inputs with their features, which are retrieved by a deep belief network(DBN). Furthermore, a new mechanism is proposed to decide whether the forecasting result is accepted or not. The model is evaluated with several datasets. The reported experiments demonstrate the superior performance of the proposed model compared to the state-of-the-art approaches.

     

GRU-based capsule network with an improved loss for personnel performance prediction

Applied Intelligence - Personnel performance is a key factor to maintain core competitive advantages. Thus, predicting personnel future performance is a significant research domain in human...     

A graph-based QoS prediction approach for web service recommendation

Applied Intelligence - With the development of the Internet, the recommendation based on Quality of Service(QoS) is proven to be an efficient way to deal with the ever-increasing web services in...     

AES算法的硬件实现分析与设计

分析了AES算法的结构特点,对算法的不同硬件实现方式进行了对比分析,分析结果表明,不同的实现方式在算法应用模式支持、运行频率、资源占用、吞吐量方面各有优缺点,需要根据具体应用需求采用合适的实现方式。对AES算法不同实现方式的分析以及提出的设计结构对于其他类似的分组密码算法实现也具有参考价值。     

基于PLC的单轨吊司控道岔系统设计

针对单轨吊道岔人工就地控制方式可能导致单轨吊频繁启停、影响运输的连贯性等问题,设计了一种基于PLC的单轨吊司控道岔系统。该系统以PLC为控制核心,实现了道岔远程司控、道岔状态指示、故障报警等功能,保障了单轨吊运输的安全性、连续性,提高了煤矿辅助运输系统的自动化程度和工作效率。     

面向IC装备功能仿真平台的多模式测试方法

为了实现全面对IC装备以及装备功能仿真平台进行功能测试,通过分析装备命令响应特点,提出了包含单指令测试、逻辑指令序列测试以及随机指令序列测试的多模式测试方法;单指令测试验证设备以及由相关设备组成的系统的单指令响应正确性;逻辑指令序列从控制系统角度验证设备或系统的连续动态运行性能;随机指令序列测试验证设备或系统的动态随机响应性能;实际应用表明,提出的测试方法可以满足IC装备功能仿真平台的测试需求。     

基于PCI总线的通用旋转变压器解算系统设计

针对轴角参量的闭环控制和检测的局限性,设计了一种基于PCI总线的通用旋转变压器解算系统;通过DDS模块和功率放大电路产生激磁信号,在FPGA的控制下,解算芯片完成轴角参量的解算;采用FIFO机制进行数据缓存,采样时标达到0.1ms;系统具有激磁信号频率0Hz到10kHz可变、速比1∶8到1∶64可调的特点,是一种精度高、通用性好的轴角检测解决方案。