scholarly journals A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies

Agriculture ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 460
Author(s):  
Renjie Huang ◽  
Tingshan Yao ◽  
Cheng Zhan ◽  
Geng Zhang ◽  
Yongqiang Zheng
Keyword(s):  
Computer Vision ◽  
Local Binary Pattern ◽  
Detection Algorithm ◽  
Simulation Experiments ◽  
Successive Sampling ◽  
Recognition Algorithms ◽  
Detection And Identification ◽  
Yellow Sticky Trap ◽  
Long Acting ◽  
Detection And Recognition

Citrus flies are important quarantine pests in citrus plantations. Electronic traps (e-traps) based on computer vision are the most popular types of equipment for monitoring them. However, most current e-traps are inefficient and unreliable due to requiring manual operations and lack of reliable detection and identification algorithms of citrus fly images. To address these problems, this paper presents a monitoring scheme based on automatic e-traps and novel recognition algorithms. In this scheme, the prototype of an automatic motor-driven e-trap is firstly designed based on a yellow sticky trap. A motor autocontrol algorithm based on Local Binary Pattern (LBP) image analysis is proposed to automatically replace attractants in the e-trap for long-acting work. Furthermore, for efficient and reliable statistics of captured citrus flies, based on the differences between two successive sampling images of the e-trap, a simple and effective detection algorithm is presented to continuously detect the newly captured citrus flies from the collected images of the e-trap. Moreover, a Multi-Attention and Multi-Part convolutional neural Network (MAMPNet) is proposed to exploit discriminative local features of citrus fly images to recognize the citrus flies in the images. Finally, extensive simulation experiments validate the feasibility and efficiency of the designed e-trap prototype and its autocontrol algorithm, as well as the reliability and effectiveness of the proposed detection and recognition algorithms for citrus flies.

Detection and Recognition of RF Devices Using Support Vector Machine

2013 ◽  
Vol 5 (4) ◽  
pp. 13-20
Author(s):  
Shikhar P. Acharya ◽  
Ivan G. Guardiola
Keyword(s):  
Support Vector Machine ◽  
Radio Frequency ◽  
Experimental Results ◽  
Support Vector ◽  
Noise Band ◽  
Detection And Identification ◽  
Electromagnetic Emissions ◽  
Rf Devices ◽  
Unintended Electromagnetic Emissions ◽  
Detection And Recognition

Radio Frequency (RF) devices produce some amount of Unintended Electromagnetic Emissions (UEEs). UEEs are generally unique to a device and can be used as a signature for the purpose of detection and identification. The problem with UEEs is that they are very low in power and are often buried deep inside the noise band. The research herein provides the application of Support Vector Machine (SVM) for detection and identification of RF devices using their UEEs. Experimental Results shows that SVM can detect RF devices within the noise band, and can also identify RF devices using their UEEs.

scholarly journals Event-triggered attack detection and identification in power-electronics-based DC microgrid

2021 ◽  
Author(s):  
Sicheng Gong
Keyword(s):  
Power Flow ◽  
Detection Algorithm ◽  
Power Line ◽  
Current Data ◽  
Attack Detection ◽  
Dc Microgrid ◽  
Detection Mechanism ◽  
Detection And Identification ◽  
Event Triggered ◽  
Identification Strategy

This paper proposes a novel event-triggered attack detection mechanism for converter-based DC microgrid system. Under a distributive network framework, each node collects its neighbours' relative data to regulate its own output for local stabilization. Without power line current data, hardly can an agent directly identify the source of unexpected power flow, especially under an organized attack composed of voltage variations and corresponding deceptive messages. In order to recognize traitors who broadcast wrong data, target at system distortion and even splitting, an efficient attack detection and identification strategy is mandatory. After the attack detector is triggered, each relative agent refuses to trust any received data directly before authentication. Through proposed two-step verification by comparing theoretical estimated signals with received ones, both self sensors and neighbour nodes would be inspected, and the attacker was difficult to hide himself. Through simulation on SIMULINK/PLECS and hardware experiments on dSpace Platform, the effectiveness of proposed detection algorithm has been proved.

scholarly journals Pengenalan Wajah Manusia berbasis Algoritma Local Binary Pattern

2017 ◽  
Vol 17 (2) ◽  
pp. 29-38
Author(s):  
Ratih Purwati ◽  
Gunawan Ariyanto
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Cross Validation ◽  
Support Vector ◽  
Fold Cross Validation

Face Recognition merupakan teknologi komputer untuk mengidentifikasi wajah manusia melalui gambar digital yang tersimpan di database. Wajah manusia dapat berubah bentuk sesuai dengan ekspresi yang dimilikinya. Wajah manusia dapat berubah bentuk sesuai dengan eskpresi yang dimilikinya. Ekspresi wajah manusia memiliki kemiripan satu sama lain sehingga untuk mengenali suatu ekspresi adalah kepunyaan siapa akan sedikit sulit. Pengenalan wajah terus menjadi topik aktif di zaman sekarang pada penelitian bidang computer vision. Penggunaan wajah manusia sering kita jumpai pada fitur-fitur aplikasi media sosial seperti Snapchat, Snapgram dari Instagram dan banyak aplikasi sosial media lainnya yang menggunakan teknologi tersebut. Pada penelitian ini dilakukan analisa pengenalan ekpresi wajah manusia dengan pendekatan fitur alogaritma Local Binary Pattern dan mencari pengembangan alogaritma dasar Local Binary Pattern yang paling optimal dengan cara menggabungkan metode Hisogram Equalization, Support Vector Machine, dan K-fold cross validation sehingga dapat meningkatkan pengenalan gambar wajah manusia pada hasil yang terbaik. Penelitian ini menginput beberapa database wajah manusia seperti JAFFE yang merupakan gambar wajah manusia wanita jepang yang berjumlah 10 orang dengan 7 ekspresi emosional seperti marah, sedih, bahagia, jijik, kaget, takut dan netral ke dalam sistem. YALE yaitu merupakan gambar wajah manusia orang Amerika. Serta menggunakan dataset CALTECH yang merupakan gambar manusia yang terdiri dari 450 gambar dengan ukuran 896 x 592 piksel dan disimpan dalam format JPEG. Kemudian data tersebut di sesuaikan dengan bentuk tekstur wajah masing-masing. Dari hasil penggabungan ketiga metode diatas dan percobaan-percobaan yang sudah dilakukan, didapatkan hasil yang paling optimal dalam pengenalan wajah manusia yaitu menggunakan dataset JAFFE dengan resolusi 92 x 112 piksel dan dengan tingkat penggunaan processor yang tinggi dapat mempengaruhi waktu kecepatan komputasi dalam proses menjalankan sistem sehingga menghasilkan prediksi yang lebih tepat.

An improved local binary pattern based edge detection algorithm for noisy images

2019 ◽  
Vol 36 (3) ◽  
pp. 2043-2054 ◽  
Author(s):  
Navdeep ◽  
Sonal Goyal ◽  
Asha Rani ◽  
Vijander Singh
Keyword(s):  
Edge Detection ◽  
Local Binary Pattern ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Noisy Images

Data-driven state detection algorithm for multi-scale systems and its application

2013 ◽  
Vol 228 (7) ◽  
pp. 1223-1234 ◽  
Author(s):  
Zeng Deliang ◽  
Liu Jiwei ◽  
Liu Jizhen
Keyword(s):  
Failure Rate ◽  
Detection Algorithm ◽  
Data Driven ◽  
Simulation Experiments ◽  
Multi Scale ◽  
State Detection ◽  
Security And Reliability ◽  
Roller Ring ◽  
Technical Basis ◽  
Over Time

To improve the security and reliability of equipment and reduce their failure rate, a data-driven state detection algorithm was proposed. The concepts of multi-scale system, multi-scale entropy and multi-scale exergy were defined. The algorithm is used for multi-scale systems whose state parameters change over time and have the characteristic of increasing monotonically on a dominant scale. An abrasion index for the middle speed roller ring mill was constructed, which was used to monitor the states of the instruments. Noise that affected the accuracy of the results was analyzed. The results of simulation experiments demonstrate the effectiveness of the algorithm, which can provide a technical basis for condition maintenance.

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