Optimasi Penempatan Lokasi Access Point dengan Metode Simulated Annealing dan Trilateration (Studi Kasus : Universitas Budi Luhur)

INTISASI Teknologi tanpa kabel yang lebih dikenal dengan Wi-Fi. Wi-Fi (Wirelles Fidelity) adalah koneksi tanpa kabel yang menghubungkan jaringan komputer, seperti ponsel yang menggunakan teknologi radio sehingga pengguna dapat melakukan transfer data dengan cepat dan aman. Kebutuhan akan Wi-Fi terlihat dengan jelas, baik di rumah, di kantor-kantor, di kampus, maupun pusat-pusat bisnis. Trafik pengguna Wi-Fi semakin tinggi, namun pemasangan Access Point belum tepat banyak access point yang terpasang yang pemasangannya tidak menentukan jarak antar access point. Menyebabkan penumpukan sinyal dengan access point lain dan area yang tidak tercover oleh access point atau area blankspot. Banyaknya pemasangan access point yang tidak tepat menjadi permasalahan yaitu koneksi Wi-Fi yang tidak stabil dan banyaknya tempat yang tidak terjangkau oleh sinyal Wi-Fi atau blankspot. Salah satu metode yang dipakai dalam penentuan pemasangan access point saat ini adalah menggunakan metode Simulated Annealing dan Trilateration. Penelitian ini akan membahas optimasi pemasangan access point dengan menggunakan metode trilateration yang digunakan untuk mengukur jarak antar access point dan Simulated Aneling digunakan untuk mengukur kekuatan sinyal Wi-Fi. Dari hasil pemasangan Access point dengan menggunakan metode trialteration dan simulated annealing dapat mengurangi area blankspot dan mengurangi penumpukan sinyal dengan access point lain. Kata Kunci : access point, Simulated Annealing, Trilateration, Sinyal, Area. ABSTRACT Wireless technology, better known as Wi-Fi. Wi-Fi (Wirelles Fidelity) is a wireless connection that connects computer networks, such as mobile phones that use radio technology so that users can transfer data quickly and safely. The need for Wi-Fi is clearly visible, both at home, in offices, on campus, and business centers. Wi-Fi user traffic is getting higher, but the installation of Access Points is not right yet, many installed access points do not determine the distance between access points. Causes signal buildup with other access points and areas not covered by the access point or blankspot area. The number of improper access point installations is a problem, namely unstable Wi-Fi connections and many places that are not covered by Wi-Fi or blankspot signals. One of the methods used in determining the installation of an access point is to use the Simulated Annealing and Trilateration method. This study will discuss the optimization of the installation of access points using the trilateration method used to measure the distance between access points and Simulated Aneling is used to measure the strength of Wi-Fi signals. From the results of installing Access Points using the method of trialteration and simulated annealing can reduce the blankspot area and reduce the buildup of signals with other access points. Keywords: access point, Trilateration, Anneling Simulation, Signal, Area.

Iterative learning control of air pressure variation inside a high-speed train under the excitation of the tunnel pressure wave with a fixed-morphologic form

Based on the characteristics that the tunnel pressure wave has a fixed-morphologic form when the same train passes through the same tunnel, an applicational approach based on the iterative learning control (ILC) is developed, aiming at overcoming the drawbacks of the traditional strategy for controlling the air pressure variation inside a high-speed train carriage. To achieve the goal, the control system is mathematically modelled. Then, the problem is formulated. The task of suppressing the influence of the tunnel pressure wave on the air pressure inside the carriages is shifted as an ILC problem of tracking the comfort index with varying trial length. The algorithm of refreshing the control signal from trial to trial is determined and the process of ILC control is designed. Next, the convergence of the newly-developed applicational ILC algorithm is discussed and the algorithm is simulated by the simulation signal and field-test signal. Results show that the applicational ILC algorithm be more adaptable in handling the control of the air pressure inside carriage under the excitation of varying-amplitude, varying-scale and varying-initial-states tunnel pressure wave. Meanwhile, the matching with tunnel pressure wave makes the applicational ILC algorithm will take both the riding comfort and fresh air into consideration, which upgrades the performances when the high-speed train passing through long tunnels.

A Segmentation Model of ECU Excitation Signal Based on Characteristic Parameters

According to the basic structure and working principle of the excitation signal sensors of a diesel engine electronic control unit (ECU), a segmentation model of an ECU excitation signal based on characteristic parameters (ESCP-SM) is proposed. In the ESCP-SM, the ECU excitation signal is divided into several parts, and each part has its characteristic parameters model. By using the same global parameters and strictly controlling each part’s proportional parameters, the ESCP-SM can achieve signal alignment and dynamic frequency modulation. Based on the simulation experiment, spectrum analysis proves that this modeling method ensures that the original signal’s effective information is not lost. Pearson similarity analysis shows that the similarity between the simulation signal and practical signal reaches 74%, exhibiting strong correlation. In addition, we set up a physical testing environment. ESCP-SM is realized based on virtual instrument technology, and provides excitation signals for a Komatsu 8 ECU. By modifying the parameter configuration, the ECU can drive the injector to work correctly.

A New Demodulation Method for Mechanical Fault Feature Extraction based on LOD and IEE

The rolling bearing and gear fault features are generally shown as modulation characteristics of their vibration signals. The empirical envelope (EE) method is an accordingly common demodulation method. However, the EE method has the defects of over- and undershoot, which may lead to demodulation error. According to this, an envelope optimization algorithm -- empirical optimal envelope (EOE) is introduced into the EE method, and an improved empirical envelope (IEE) method is obtained to calculate the instantaneous amplitude and instantaneous frequency of mono-component modulation signal. Furthermore, aiming at the actual measured mechanical vibration signal has multi-component modulation feature, the IEE method is combined with an adaptive signal decomposition method -- local oscillatory characteristic decomposition (LOD) proposed by the author, thereby a new multi-component signal demodulation method based on LOD and IEE is proposed. The proposed method is compared with Hilbert transform (HT) and Teager energy operator (TEO) demodulation methods by the simulation signal and actual measured mechanical vibration signal. The results show that the demodulation effects including edge effects, negative frequency, over- and undershoot of the proposed method are significantly improved and can extract the rolling bearing and gear fault feature information clearly.

Impulse Signal Detection for Bearing Fault Diagnosis via Residual-Variational Mode Decomposition

A novel method named residual-variational mode decomposition (RVMD) is proposed in this study to extract bearing fault features accurately. RVMD can determine the number of modes and the balance parameter adaptively, and it has two stages. In the first stage, the signal is decomposed into a series of modes until the correlation coefficient between the raw signal and the decomposition results reaches the threshold. A redefined kurtosis, which can resist the interferences from aperiodic impulse efficiency, is applied to rebuild the ensemble kurtosis index. The mode that has the largest rebuild-ensemble kurtosis, and its neighbors, are kept. By putting the residual signal into the second stage, an iteration process is applied to determine the optimal parameters for variational mode decomposition (VMD). VMD is re-run with the optimal parameters, and the sub-mode filtered with the larger rebuild-ensemble kurtosis is examined by the envelope analysis technology to observe the fault feature. The effectiveness of RVMD is verified by the simulation signal and three experiment signals. Its superiority is shown by comparing it with some existing methods.

Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework

Symplectic geometric mode decomposition (SGMD) is a newly proposed signal processing method. Because of its superiority, it has gained more and more attention in the field of fault diagnosis. However, the similar component reorganization problem involved in this method has not been clearly stated. Aiming at this problem, this paper proposes the SGMD-CS method based on the SGMD method and the cosine similarity (CS) and has been compared and verified on the simulation signal and the actual rolling bearing signal. In addition, in order to realize the intelligent diagnosis of the wind turbine bearing fault, the symplectic geometric entropy (SymEn) is extracted as the fault feature and input it into the AdaBoost classification model. In summary, this paper proposes a new wind turbine fault feature extraction method based on the SGMD-CS and AdaBoost framework, and the validity of the method is verified by the rolling bearing vibration data of the Electrical Engineering Laboratory of Case Western Reserve University.

Extraction method of composite fault features of gear transmission system based on demodulation of NMD and Teager energy operators

In order to effectively identify and extract the composite fault characteristics of the gear transmission system, a composite fault diagnosis method combining nonlinear mode decomposition (NMD) and Teager energy operator demodulation is proposed. Because the envelope demodulation of Hilbert transform has the disadvantages of large amount of calculation and end effect, it uses Teager energy operator to solve the problem of large amount of calculation, and NMD solves the problem that the fault signal features is not easy to extract under the mode aliasing. First, the NMD method is used to decompose the fault simulation signal, and the nonlinear modal component with practical physical significance is obtained. Secondly, the Teager energy operator demodulation is carried out for the nonlinear modal components, and the demodulation results are analyzed to verify the feasibility of the method. Then, the method is applied to the composite fault diagnosis of gear pitting wear in gear transmission system, and the characteristic frequency obtained from the test data is compared with the calculated characteristic frequency. The comparative analysis shows that the method can separate the fault characteristic frequencies of large and small gears. The comparative analysis with EMD and EEMD methods in simulation signal analysis and experimental research shows that this method is superior.

Study on Fault Diagnosis Method of Planetary Gearbox Based on Turn Domain Resampling and Variable Multi-Scale Morphological Filtering

The turn domain resampling (TDR) method is proposed in the paper on the basis of the existing angle domain resampling for solving the problem of non-fixed fault frequency under variable working conditions. TDR can select the appropriate sampling order according to the influence of frequency conversion, which avoided the error caused by the spline interpolation method. It can provide accurate parameters for the subsequent calculation of the equivalent frequency order. Variable multi-scale morphological filtering (VMSMF) method is proposed for the purpose of further reducing the interference of noise in resampling signal to feature extraction. VMSMF adaptively selects structural elements according to the parameter change of impact signal to make its scale more targeted. It only needs to calculate once using the optimal structural unit for a particular impact, and the filtering accuracy and operating efficiency have been greatly improved. The main steps of this article are as follows. First, the TDR is used to resample the original signal as to get the resampling signal which is still submerged by the strong noise. In the second step, VMSMF is used to filter the resampling signal to obtain the signal with less noise interference. Finally, the fault characteristics of the filtering signal was extracted and compared with the possible fault frequency calculated by the sampling parameters provided by resampling, so as to determine the fault type of the planetary gearbox. By analyzing the simulation signal and the experimental signal respectively, this method can find out the corresponding fault characteristics effectively.

Reconstruction of Periodic Band Limited Signals from Non-Uniform Samples with Sub-Nyquist Sampling Rate

Important state parameters, such as torque and angle obtained from the servo control and drive system, can reflect the operating condition of the equipment. However, there are two problems with the information obtained through the network from the control and drive system: the low sampling rate, which does not meet the sampling theorem and the nonuniformity of the sampling points. By combing equivalent sampling and nonuniform signal reconstruction theory, this paper proposes a reconstruction method for signal obtained from servo system in periodic reciprocating motion. Equivalent sampling combines the low rate and nonuniform samples from multiple periods into one single period, so that the equivalent sampling rate is far increased. Then, the nonuniform samples with high density are further resampled to meet the reconstruction conditions. This step can avoid the amplitude error in the reconstructed signal and increase the possibility of successful reconstruction. Finally, the reconstruction formula derived from basis theory is applied to recover the signal. This method has been successfully verified by the simulation signal of the robot swing process and the actual current signal collected on the robot arm testbed.