Papers by Keyword: Detection

Abstract: This study aims to optimize the protection of photovoltaic (PV) systems against electrical disturbances, particularly voltage dips. The objective is to develop new methods for analyzing and managing these disturbances, which affects the power quality in electrical networks and causes the automatic disconnection of PV systems, leading to production losses and plant malfunctions. In addition, voltage dips represent a major challenge for industrial sectors, where they can cause production interruptions and process malfunctions, leading to economic losses and product quality degradation. This research proposes a method for real-time detection of voltage dips, by integrating the recloser settings within the monitoring system. This approach makes it possible to distinguish temporary outages, related to reclosing, from prolonged outages, thus avoiding unnecessary disconnections of PV systems. The method's performance has been validated by simulations carried out in the MATLAB/SIMULINK environment.

Abstract: In Nigeria, a crucial responsibility of the executive arms of the government is to submit annual budgetary allocations to the national assembly for approval. Due to the diversity and complexity of the budget, the national assembly is mandated to carry out its constitutional duty of scrutinizing the budget to discover irregularity or anomaly, make recommendations, or substantial modification upon what it received. This is very challenging, particularly in Nigeria where there are many different ethnicities and regional, to ensure inclusiveness, the national assembly must carry out its constitutional duty diligently and carefully without fear or favor that often has unintended consequences. This might not be very easy to accomplish within a short period. Thus, this research aims at detecting an anomaly in the budget that will ease the legislative duty thereby facilitating the process of appropriation. The concept of Clustering for Machine learning technique was used for the detection of an anomaly, where the detected ones are noted and indicated for critical examination.

Abstract: A preliminary study on the selective detection of hypochlorite using gold nanoparticles (AuNPs) has been carried out. Gold nanoparticles have been synthesized using sodium citrate as capping and stabilizing agent simultaneously at room temperature with no stirring and pH adjustment. Development of hypochlorite detection methods is based on the ability of hypochlorite to oxidize L-cysteine that can aggregate AuNPs through the formation of S‒Au bonds. The aggregated gold nanoparticles will change color from red for the original AuNPS to blue for the aggregated AuNPs. The presence of hypochlorite added to L-cysteine will oxidize the thiol group of L-cysteine thereby reducing the ability of L-cysteine to aggregate AuNPs. The higher the concentration of hypochlorite in L-cysteine, the more thiol groups are oxidized. Thus the presence of hypochlorite will act as anti-aggregation of L-cysteine-induced aggregation of AuNPs and therefore the color of solution is turned back to red from blue. This color change can be easily visualized by naked eye within 7 min. The existence of AuNPs, L-cysteine aggregated AuNPs, and AuNPs that have been used to detect hypochlorite have been seen using UV-Vis spectrophotometers and Transmission electron microscopy (TEM).

Abstract: The fourth most common form of cancer among women is cervical cancer with 569,847new cases and 311,365 reported deaths worldwide in 2018. Cervical cancer is classified as the third leading cause of cancer among women in Malaysia, with approximately 1,682 new cervical cases and about 944 deaths occurred in 2018. Cervical cancer can be detected early by cervical cancer screening. Papanicolaou test, also known as Pap smear test is conducted to detect cancer or precancer in the cervix. The disadvantage of this conventional method is that the sample of microscopic images will risk blurring effects, noise, shadow, lighting and artefact problems. The diagnostic microscopic observation performed by a microbiologist is normally time-consuming and may produce inaccurate results even by experienced hands. Thus, correct diagnosis information is essential to assist physicians to analyze the condition of the patients. In this study, an automatedsegmentation system is proposed to be used as it is more accurate and faster compared to the conventional technique. Using the proposed method in this paper, the image was enhanced by applying a median filter and Partial Contrast Stretching. A segmentation method based on mathematical morphology was performed to segment the nucleus in the Pap smear images. Image Quality Assessment (IQA) which measures the accuracy, sensitivity and specificity were used to prove the effectiveness of the proposed method. The results of the numerical simulation indicate that the proposed method shows a higher percentage of accuracy and specificity with 93.66% and 95.54% respectively compared to Otsu, Niblack and Wolf methods. As a conclusion, the percentage of sensitivity is slightly lower, with 89.20% compared to Otsu and Wolf methods. The results presented here may facilitate improvements in the detection performance in comparison to the existing methods.

Abstract: A method for the identification of graphene materials in fibers by high-resolution transmission electron microscopy (HRTEM)-Energy Dispersive Spectroscopy (EDS) has been reported. Two ways to prepare samples are available, namely the dissolution extraction and the ultra-thin sectioning method. For samples prepared by any method, the graphene material in the fiber can be detected by the following steps. Firstly,the elemental composition of the microparticle is demonstrated by EDS. Secondly, the morphology of the particles in the fiber can be obtained by TEM, and the number of layers of graphene materials is able to observed directly from the edge of sheet.

Abstract: Breast cancer is the utmost female tumor and the primary cause of deaths among female. Computer-Aided Detection (CAD) systems are widely used as a tool to detect and classify the abnormalities found in the mammographic images. A detection of breast tumor in a mammogram has been a challenge due to the different intensity distribution which leads to the misdiagnosis of breast cancer. This research proposes a dectection system that is capable to detect the presence of mass tumor from a mammogram image. A total of 160 mammogram images are acquired from Mammographic Image Analysis Society (MIAS) databse, which are 80 normal and 80 abnormal images. The mammogram images are rescaled to 300 x 300 resolution. The noise in the mammogram is suppressed by using a Wiener filter. The images are enhanced by using Power Law (Gamma) Transformation, ɣ = 2 for a better image quality. The greyscale information that contain tumor mass is extracted and used to model the proposed detection system by using 80% or 128 and of the total 160 mammogram images. The rest 20% or 32 mammogram images are used to test the performance of the proposed detection system. The experimental results show that performance of the proposed detection system has 90.93% accuracy.

Abstract: In this study, the colorimetric performance of unmodified citrate-stabilized silver nanoparticles (cit-AgNP) for Cu²⁺ detection was investigated. Cit-AgNP was successfully synthesized using the modified Creighton method with sodium borohydride as reducing agent and trisodium citrate as stabilizing agent. The resulting nanoparticle was yellow in color, characteristic of AgNP. The absorbance peak was determined at 400 nm using UV Vis analysis while for morphology, the particles were spherical in shape with an average diameter of 11 nm determined by TEM analysis. In the presence of increasing Cu²⁺ concentration, the yellow cit-AgNP turned orange and showed decreasing absorbance at 400 nm with simultaneous emergence of additional peak at 450 nm. These changes were attributed to the nanoparticle aggregation confirmed by TEM analysis. A calibration curve generated showed that the absorbance ratio 450/400 nm is directly proportional to Cu²⁺ concentration from 0 to 40x10^-4 M with good linear fit at R² = 0.9749. The detection and quantification limits were determined to be 6.59x10^-4 M and 21.97x10^-4 M, respectively. Overall, the study demonstrated the potential of the assay for Cu²⁺ sensing application.

Abstract: Based on the finite element model, the propagation characteristics of Rayleigh wave in layered structure is studied in this paper, the time-domain characteristics of wave form is analysed under different working conditions, and the identification parameters of surface wave method to detect the layered concrete is proposed. When the incident elastic wave propagates to the defect, due to the barrier effect of the defect, a part of the incident R wave is converted into a reflected R wave, which propagates along the track plate to the surface; the other part of the R wave is converted into a transmitted R wave, along the concrete. The energy amplitude can be used as one of the parameters to identify defects in the layered concrete structure.

Abstract: Ultrasonic measurement is used to evaluate wrinkles during sheet metal forming. The authors developed a new apparatus for investigating the relationship between the wrinkles in press forming and the ultrasonic reflection characteristic of an angle beam. The new evaluation apparatus was composed of probe-fixing parts, an upper die, a middle die, and a lower die. A specimen was sandwiched by a pair of dies in the evaluation apparatus. Angle beam probes for transmission and reception were set on the upper die. Specimens in the form of plates having periodic trapezoidal wrinkles were fabricated by electro-discharge machining. In the new evaluation apparatus, the specimen is placed in contact with the die. Wrinkling was found to change the ultrasonic reflection characteristic of an angle beam. The new apparatus can thus be used to evaluate wrinkles using the angle beam technique.

Abstract: Retinal fundus image is important for the ophthalmologist to identify and detect many vision-related diseases, such as diabetes and hypertension. From an acquisition process, retinal images often have low gray level contrast and low dynamic range. This problem may seriously affect the diagnostic process and its outcome, especially if an automatic computer-based procedure is used to derive diagnostic parameters. In this paper, a new proposed method based on statistical information such as mean and standard deviation was studied. The combination of local and global technique was successful to detect the luminosity region. Then, a simple correction intensity equation was proposed in order to replace the problem intensity. The results of the numerical simulation (SNR = 2.347 and GCF = 4.581) indicate that the proposed method effective to enhance the luminosity region. Implications of the results and future research directions are also presented. Keywords: Detection, Luminosity, Retinal, Statistical.