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Research of Integrated Marine Environment Information Support and Forecast System
Published in Lin Mu, Lizhe Wang, Mingwei Wang, Information Engineering for Ports and Marine Environments, 2020
Lin Mu, Lizhe Wang, Mingwei Wang
Java programming language is very close to C and C+ languages in style. As a pure object-oriented programming language, Java inherits the essence of object-oriented technology of C++ language. Java saves programmers the trouble of memory management by abandoning the pointer (replaced with reference), operator overloading and multiple inheritance (replaced with interface) of the C ++ language which are prone to cause error and adding a garbage collector for collecting memory space taken up by objects that are no longer referenced. In the Java 1.5 version, generic programming, type-safe enumeration, variable-length argument and autoboxing/unboxing and other language features are introduced. Unlike common compiled execution computer language and interpretive execution computer language, Java compiles the source code to binary bytecode before interpreting and executing the bytecode via various virtual machines on different platforms, realizing the cross-platform characteristic of “write once, run anywhere.” However, each execution of the compiled bytecode takes a certain amount of time, which reduces the operation efficiency of Java to a certain degree.
File Input and Output
Published in David E. Hiebeler, R and MATLAB®, 2018
In R, you can open a file for reading via the command . You can test for success by calling , which returns TRUE if the file was successfully opened. In MATLAB, you can use . It will return a positive integer upon success, and -1 on failure. In MATLAB, the string is optional, as that is the default.2 Also in both platforms, be aware that if you open a file for writing (using either or , the file will be overwritten, i.e., any existing data in them will be erased without warning! When you are done accessing a file, you should close its file descriptor. In R, you can do this via , and in MATLAB, you can use .
Detecting Android Kernel Rootkits via JTAG Memory Introspection
Published in Georgios Kambourakis, Asaf Shabtai, Constantinos Kolias, Dimitrios Damopoulos, Intrusion Detection and Prevention for Mobile Ecosystems, 2017
Mordechai Guri, Yuri Poliak, Bracha Shapira, Yuval Elovici
The first rootkit was implemented as a kernel module (syscallTableHook.ko) which modifies the address of four system call addresses in the system call table: read(), write(), open(), and close(). We chose four basic system functions that can be used maliciously in order to intercept the file system, sensors, and network access operations. The experiment starts by executing a PRACTICE script to get a binary snapshot of the kernel's system call table before and after the execution of the rootkit. In Figure 7.4, we see the two snapshots of the system call table in a binary form of the hex editor viewer. As can be seen, four modified addresses in the table have been detected. The original addresses of the system calls are marked in blue, while the modified addresses are marked in red. In the next step, the script checks which system calls have been changed. This is achieved by parsing the header file (unistd.h) from the source tree of the Android kernel. This file contains the order and names of the system call functions in the table. Next, the Python script receives the two snapshots of the system call tables and the list of functions from the kernel and returns the names of functions that have been modified. The output of the system is shown in Figure 7.5.
Tracing morphological characteristics of activated sludge flocs by using a digital microscope and their effects on sludge dewatering and settling
Published in Environmental Technology, 2023
Yuki Nakaya, Jinming Jia, Hisashi Satoh
The images were subsequently processed using Image-Pro Plus 6.0 (Media Cybernetics, Inc., Maryland, U.S.A.). First, small debris and filamentous bacteria were discriminated from AS flocs as follows. Filtration using a close filter with a 2 × 2 kernel was repeated twice. This process fills gaps of dark objects and connects nearby bright objects via the magnification of bright areas (protrusions) that penetrate the dark objects. Therefore, the colours of filamentous bacteria and weak flocs, which are small with an elongated shape, are processed to be similar to the bright background. Finally, by using a built-in colour-histogram-based edge detection algorithm, the filamentous bacteria and weak flocs were not recognized as dark objects, and only a core area of AS with a dark colour was recognized as presenting objects, which were named as high-density flocs (Figure 1).
On a problem for the nonlinear diffusion equation with conformable time derivative
Published in Applicable Analysis, 2022
Vo Van Au, Dumitru Baleanu, Yong Zhou, Nguyen Huu Can
By an argument in the one above, to establish a regularized solution, we need to find a new operator, such as such that is bounded operator. For let us define The basic idea of regularization method is constructive the kernel must satisfy two properties: (i) Property (I): If is fixed, the kernel is bounded. (ii) Property (II): If the parameter is small, the kernel is close to 1.
Intelligent machine fault diagnosis with effective denoising using EEMD-ICA- FuzzyEn and CNN
Published in International Journal of Production Research, 2022
Hanting Zhou, Wenhe Chen, Changqing Shen, Longsheng Cheng, Min Xia
In this paper, a CNN-based approach with a two-stage joint denoising method was proposed for intelligent fault diagnosis of rotating machinery. Effective signal denoising was achieved to increase the diagnosis accuracy and robustness. EEMD was used to decompose a single-channel signal into multiple independent components to satisfy the assumption of ICA, with the FuzzyEn as the threshold criterion to identify the noise signal. Experimental studies on roller bearings verified the diagnosis performance of the proposed approach. The denoised signal is close to the noisy signal with maximum correlation and minimum error. Moreover, accuracy can reach over 96.91% under different noisy conditions, especially in extreme noise environments. With effective denoising, the proposed 2D CNN model can achieve over 99.75% accuracy and 98.75% F1 score under variable noise environments. The comparison with the denoising methods and the state-of-the-art diagnosis methods showed that the proposed method can achieve more accurate and robust performance. Also, the remarkable anti-interference capability of the proposed diagnosis framework would enable its wide application in machinery fault diagnosis under strong noise environments.