The integration of machine learning (ML) techniques is now indispensable in healthcare, especially in addressing the challenges posed by chronic illnesses, which present a significant global health concern due to their unpredictable nature. This study compares ML techniques employed in the diagnosis and treatment of chronic conditions such as diabetes, liver disease, thyroid disease, breast cancer, heart disease, Alzheimer’s disease, and others. Two primary criteria guided the selection of diseases under investigation. Firstly, those extensively studied with ML methods, and secondly, those leveraging ML models to resolve issues or yield promising results. The research concludes that in real-time clinical practice, there is no universally proven method for selecting the optimal course of action due to each method’s unique advantages and disadvantages. While a hybrid technique may exhibit slightly slower speed growth, it holds the potential to enhance the accuracy and performance of a model.
目前，机器学习（ML）技术的整合已成为医疗保健领域不可或缺的一部分，尤其是在应对慢性疾病带来的挑战方面。本研究比较了在糖尿病、肝病、甲状腺疾病、乳腺癌、心脏病、阿尔茨海默病等慢性疾病的诊断和治疗中使用的 ML 技术。选择调查疾病有两个主要标准。首先是那些用 ML 方法进行过广泛研究的疾病，其次是那些利用 ML 模型解决问题或产生有希望结果的疾病。研究得出的结论是，在实时临床实践中，由于每种方法都有其独特的优缺点，因此在选择最佳行动方案方面没有普遍适用的方法。虽然混合技术的速度增长可能稍慢，但它有可能提高模型的准确性和性能。
{"title":"Reviewing chronic ailments: predicting diseases with a multi-symptom approach","authors":"Aicha Oussous, Abderrahmane Ez-Zahout, Soumia Ziti","doi":"10.11591/ijeecs.v35.i1.pp418-427","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp418-427","url":null,"abstract":"The integration of machine learning (ML) techniques is now indispensable in healthcare, especially in addressing the challenges posed by chronic illnesses, which present a significant global health concern due to their unpredictable nature. This study compares ML techniques employed in the diagnosis and treatment of chronic conditions such as diabetes, liver disease, thyroid disease, breast cancer, heart disease, Alzheimer’s disease, and others. Two primary criteria guided the selection of diseases under investigation. Firstly, those extensively studied with ML methods, and secondly, those leveraging ML models to resolve issues or yield promising results. The research concludes that in real-time clinical practice, there is no universally proven method for selecting the optimal course of action due to each method’s unique advantages and disadvantages. While a hybrid technique may exhibit slightly slower speed growth, it holds the potential to enhance the accuracy and performance of a model.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141703939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp70-77
S. I. Ismail, N. Ismail, Aisyah Hannah Mohd Zaki, Suziana Omar, Syazilawati Mohamed
Gadgets have certainly become an integral part of our daily lives. From smartphones and tablets to laptops and smartwatches, we rely on these devices to stay connected, entertained, and productive throughout the day. Excessive usage of gadgets for a long time and unhealthy habits will lead to health problems such as myopia. Using gadgets at a close distance is one of the most common unhealthy habits among gadget users, especially children. This study, called "smart distance alert system" is developed to address the unhealthy habit of using gadgets at a close distance. The developed prototype operates by measuring the distance between the user and the gadget screen using an ultrasonic sensor. The buzzer and vibration motor work as an alert system, activating when the distance is less than 50 cm. Parents or guardians will get notifications through the Blynk application. The entire prototype is controlled by NodeMicrocontroller unit.
{"title":"Smart distance alert system with Blynk integration for safer gadget use","authors":"S. I. Ismail, N. Ismail, Aisyah Hannah Mohd Zaki, Suziana Omar, Syazilawati Mohamed","doi":"10.11591/ijeecs.v35.i1.pp70-77","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp70-77","url":null,"abstract":"Gadgets have certainly become an integral part of our daily lives. From smartphones and tablets to laptops and smartwatches, we rely on these devices to stay connected, entertained, and productive throughout the day. Excessive usage of gadgets for a long time and unhealthy habits will lead to health problems such as myopia. Using gadgets at a close distance is one of the most common unhealthy habits among gadget users, especially children. This study, called \"smart distance alert system\" is developed to address the unhealthy habit of using gadgets at a close distance. The developed prototype operates by measuring the distance between the user and the gadget screen using an ultrasonic sensor. The buzzer and vibration motor work as an alert system, activating when the distance is less than 50 cm. Parents or guardians will get notifications through the Blynk application. The entire prototype is controlled by NodeMicrocontroller unit.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141700186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp238-246
C. V. K. N. S. N. Moorthy, M. Tripathi, Manjunath R. Hudagi, Lingaraj A. Hadimani, Gayatri Sanjay Chavan, Sanjeevkumar Angadi
This paper presents the framework for identifying materials using a fused descriptor-based approach, leverage computer vision techniques. The system is structured into three phases: derivation, extraction, and portrayal. Initially, the system employs K-means gathering techniques for establishing derivation. Following derivation, the system utilizes variety, texture, and shape-based feature extraction methods to extract relevant features from the soluble solid content and total acid content using real-time visual inspection system. A “consolidating” fusion feature is explored in the final phase using classification algorithms like C4.5, support vector machines (SVM), and k-nearest neighbors (KNN). The performance evaluation of the recognition system demonstrates promising results, with accuracy rates of 97.89%, 94.60%, and 90.25% achieved by using C4.5, SVM, and KNN separately. This indicates that the proposed fusion strategy effectively supports accurately recognizing materials using a fused descriptor-based approach.
{"title":"Identification of soluble solid content and total acid content using real-time visual inspection system","authors":"C. V. K. N. S. N. Moorthy, M. Tripathi, Manjunath R. Hudagi, Lingaraj A. Hadimani, Gayatri Sanjay Chavan, Sanjeevkumar Angadi","doi":"10.11591/ijeecs.v35.i1.pp238-246","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp238-246","url":null,"abstract":"This paper presents the framework for identifying materials using a fused descriptor-based approach, leverage computer vision techniques. The system is structured into three phases: derivation, extraction, and portrayal. Initially, the system employs K-means gathering techniques for establishing derivation. Following derivation, the system utilizes variety, texture, and shape-based feature extraction methods to extract relevant features from the soluble solid content and total acid content using real-time visual inspection system. A “consolidating” fusion feature is explored in the final phase using classification algorithms like C4.5, support vector machines (SVM), and k-nearest neighbors (KNN). The performance evaluation of the recognition system demonstrates promising results, with accuracy rates of 97.89%, 94.60%, and 90.25% achieved by using C4.5, SVM, and KNN separately. This indicates that the proposed fusion strategy effectively supports accurately recognizing materials using a fused descriptor-based approach.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141691346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp140-147
Akmal Razak, Farah Shahnaz Feroz, Siva Kumar Subramaniam, F. Shahbodin, S. Rajkumar
The analysis of brain signals and their properties yields significant insights into the fundamental neural impairments associated with attention bias in individuals suffering from public speaking anxiety (PSA). This study aims to identify electroencephalogram (EEG) and performance biomarkers of attention bias in individuals with public speaking anxiety using the ex-Gaussian modeling technique, frontal alpha asymmetry (FAA) and delta-beta correlation (DBC). 12 subjects with high (H) PSA and 12 subjects with low (L) PSA performed the modified emotional stroop task. EEG data were captured using the low-cost 14-channel emotiv Epoc+. Results showed that the ex-Gaussian sigma was higher in the emotional condition in the high public speaking anxiety (HPSA) group, indicating attention bias. The study also found higher right FAA in HPSA compared to LPSA group. There was a negative correlation between σ and alpha power in the left region of the brain in the HPSA group, potentially related to attentional bias. Moreover, there was a notable trend towards significantly heightened DBC in the frontal and central regions of the brain among HPSA subjects. In conclusion, in biomedical engineering, the ex-Gaussian model, FAA and DBC are useful because they can identify EEG and performance biomarkers of attention bias in people with PSA.
{"title":"Biomarkers of attention bias during public speaking anxiety","authors":"Akmal Razak, Farah Shahnaz Feroz, Siva Kumar Subramaniam, F. Shahbodin, S. Rajkumar","doi":"10.11591/ijeecs.v35.i1.pp140-147","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp140-147","url":null,"abstract":"The analysis of brain signals and their properties yields significant insights into the fundamental neural impairments associated with attention bias in individuals suffering from public speaking anxiety (PSA). This study aims to identify electroencephalogram (EEG) and performance biomarkers of attention bias in individuals with public speaking anxiety using the ex-Gaussian modeling technique, frontal alpha asymmetry (FAA) and delta-beta correlation (DBC). 12 subjects with high (H) PSA and 12 subjects with low (L) PSA performed the modified emotional stroop task. EEG data were captured using the low-cost 14-channel emotiv Epoc+. Results showed that the ex-Gaussian sigma was higher in the emotional condition in the high public speaking anxiety (HPSA) group, indicating attention bias. The study also found higher right FAA in HPSA compared to LPSA group. There was a negative correlation between σ and alpha power in the left region of the brain in the HPSA group, potentially related to attentional bias. Moreover, there was a notable trend towards significantly heightened DBC in the frontal and central regions of the brain among HPSA subjects. In conclusion, in biomedical engineering, the ex-Gaussian model, FAA and DBC are useful because they can identify EEG and performance biomarkers of attention bias in people with PSA.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141702350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp570-582
Nabila Ounasser, Maryem Rhanoui, M. Mikram, B. El Asri
Anomaly detection in medical imaging is a complex challenge, exacerbated by limited annotated data. Recent advancements in generative adversarial networks (GANs) offer potential solutions, yet their effectiveness in medical imaging remains largely uncharted. We conducted a targeted exploration of the benefits and constraints associated with GAN-based anomaly detection techniques. Our investigations encompassed experiments employing eight anomaly detection methods on three medical imaging datasets representing diverse modalities and organ/tissue types. These experiments yielded notably diverse results. The results exhibited significant variability, with metrics spanning a wide range (area under the curve (AUC): 0.475-0.991; sensitivity: 0.17-0.98; specificity: 0.14-0.97). Furthermore, we offer guidance for implementing anomaly detection models in medical imaging and anticipate pivotal avenues for future research. Results unveil varying performances, influenced by factors like dataset size, anomaly subtlety, and dispersion. Our findings provide insights into the complex landscape of anomaly detection in medical imaging, offering recommendations for future research and deployment.
{"title":"Advancing medical imaging with GAN-based anomaly detection","authors":"Nabila Ounasser, Maryem Rhanoui, M. Mikram, B. El Asri","doi":"10.11591/ijeecs.v35.i1.pp570-582","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp570-582","url":null,"abstract":"Anomaly detection in medical imaging is a complex challenge, exacerbated by limited annotated data. Recent advancements in generative adversarial networks (GANs) offer potential solutions, yet their effectiveness in medical imaging remains largely uncharted. We conducted a targeted exploration of the benefits and constraints associated with GAN-based anomaly detection techniques. Our investigations encompassed experiments employing eight anomaly detection methods on three medical imaging datasets representing diverse modalities and organ/tissue types. These experiments yielded notably diverse results. The results exhibited significant variability, with metrics spanning a wide range (area under the curve (AUC): 0.475-0.991; sensitivity: 0.17-0.98; specificity: 0.14-0.97). Furthermore, we offer guidance for implementing anomaly detection models in medical imaging and anticipate pivotal avenues for future research. Results unveil varying performances, influenced by factors like dataset size, anomaly subtlety, and dispersion. Our findings provide insights into the complex landscape of anomaly detection in medical imaging, offering recommendations for future research and deployment.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141715709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp459-466
Deepthi D. Kulkarni, V. V. Dixit, Shweta Shirish Deshmukh
The field of emotion research facilitates the development of several applications, all of which aim to precisely and swiftly identify emotions. Speech and facial expressions are the main focus of typical emotion analysis, although they are not accurate indicators of true feelings. Signal analysis, namely the electroencephalograph (EEG) of the brain signals, is the other area in which emotions are analyzed. When compared to other modalities, EEG offers precise and comprehensive data that facilitates the estimation of emotional states. In order to categories the emotions using an EEG signal, this work suggests a hybrid classifier (HC). The input EEG data is preprocessed using the wiener filtering approach to extract the original information from the noisy signal. The preprocessed signal is used to extract features, such as entropy and a new hybrid model that includes models such as Bi-directional long short-term memory (Bi-LSTM) and improved recurrent neural networks (IRNN), which trains using the retrieved features, is included as part of the classification process. Happy, sad, calm, and angry are the categorization findings; the suggested work demonstrates more accurate classification results than the traditional approaches. All these are done on DEAP dataset with 60%, 70%, 80%, and 90% training sets and also a new DOSE dataset is been created similar to DEAP dataset.
{"title":"Emotion detection using EEG: hybrid classification approach","authors":"Deepthi D. Kulkarni, V. V. Dixit, Shweta Shirish Deshmukh","doi":"10.11591/ijeecs.v35.i1.pp459-466","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp459-466","url":null,"abstract":"The field of emotion research facilitates the development of several applications, all of which aim to precisely and swiftly identify emotions. Speech and facial expressions are the main focus of typical emotion analysis, although they are not accurate indicators of true feelings. Signal analysis, namely the electroencephalograph (EEG) of the brain signals, is the other area in which emotions are analyzed. When compared to other modalities, EEG offers precise and comprehensive data that facilitates the estimation of emotional states. In order to categories the emotions using an EEG signal, this work suggests a hybrid classifier (HC). The input EEG data is preprocessed using the wiener filtering approach to extract the original information from the noisy signal. The preprocessed signal is used to extract features, such as entropy and a new hybrid model that includes models such as Bi-directional long short-term memory (Bi-LSTM) and improved recurrent neural networks (IRNN), which trains using the retrieved features, is included as part of the classification process. Happy, sad, calm, and angry are the categorization findings; the suggested work demonstrates more accurate classification results than the traditional approaches. All these are done on DEAP dataset with 60%, 70%, 80%, and 90% training sets and also a new DOSE dataset is been created similar to DEAP dataset.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141703415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp551-569
M. A. Ahmed, Mohammad Naved Qureshi, Mohammad Sarosh Umar, Mouna Bedoui
Melanoma is a highly malignant skin cancer that may be fatal if not promptly detected and treated. The limited availability of high-quality melanoma images, which are needed for training machine learning models, is one of the obstacles to detecting melanoma. Generative adversarial networks (GANs) have grown in popularity as a strong technique for image synthesis. This research is also targeted at the sustainable development goal (SDG) for health care. In this study, we survey existing GAN-based melanoma image synthesis methods. In this work, we briefly introduce GANs and how they may be used for generating synthetic images. Ensuring healthy lifestyles and promoting well-being for everyone, regardless of age, is the main aim. A comparative study is carried out on how GANs are used in current research to generate melanoma images and how they improve the classification performance of neural networks. Various public and proprietary datasets for training GANs in melanoma image synthesis are also discussed. Lastly, we assess the examined studies' performance using measures like the Frechet Inception distance (FID), Inception score, structural similarity ındex (SSIM), and various classification performance metrics. We compare the evaluated findings and suggest further GAN-based melanoma image-creation research.
黑色素瘤是一种高度恶性的皮肤癌，如果不能及时发现和治疗，可能会致命。训练机器学习模型所需的高质量黑色素瘤图像有限，这是检测黑色素瘤的障碍之一。生成式对抗网络（GANs）作为一种强大的图像合成技术越来越受欢迎。这项研究也是针对医疗保健的可持续发展目标（SDG）。在本研究中，我们调查了现有的基于 GAN 的黑色素瘤图像合成方法。在这项工作中，我们简要介绍了 GAN 以及如何将其用于生成合成图像。确保健康的生活方式和促进每个人的福祉是我们的主要目标，无论年龄大小。我们对当前研究中如何使用 GANs 生成黑色素瘤图像以及它们如何提高神经网络的分类性能进行了比较研究。此外，还讨论了用于黑色素瘤图像合成中 GANs 训练的各种公共和专有数据集。最后，我们使用弗雷谢特起始距离（FID）、起始分数、结构相似性指数（SSIM）和各种分类性能指标等指标来评估所研究的性能。我们比较了评估结果，并建议进一步开展基于 GAN 的黑色素瘤图像创建研究。
{"title":"Melanoma image synthesis: a review using generative adversarial networks","authors":"M. A. Ahmed, Mohammad Naved Qureshi, Mohammad Sarosh Umar, Mouna Bedoui","doi":"10.11591/ijeecs.v35.i1.pp551-569","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp551-569","url":null,"abstract":"Melanoma is a highly malignant skin cancer that may be fatal if not promptly detected and treated. The limited availability of high-quality melanoma images, which are needed for training machine learning models, is one of the obstacles to detecting melanoma. Generative adversarial networks (GANs) have grown in popularity as a strong technique for image synthesis. This research is also targeted at the sustainable development goal (SDG) for health care. In this study, we survey existing GAN-based melanoma image synthesis methods. In this work, we briefly introduce GANs and how they may be used for generating synthetic images. Ensuring healthy lifestyles and promoting well-being for everyone, regardless of age, is the main aim. A comparative study is carried out on how GANs are used in current research to generate melanoma images and how they improve the classification performance of neural networks. Various public and proprietary datasets for training GANs in melanoma image synthesis are also discussed. Lastly, we assess the examined studies' performance using measures like the Frechet Inception distance (FID), Inception score, structural similarity ındex (SSIM), and various classification performance metrics. We compare the evaluated findings and suggest further GAN-based melanoma image-creation research.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141697910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp113-123
A. Asset, Madina Mansurova, Vadim Zhmud, A. Kopesbaeva, Nurbolat Dzheksenbaev
In some cases, the object model is a set of parallel models of the same general appearance, but with different parameters. The most common model is a model in the form of a serial connection of a first- or second-order filter and a delay link. An example is the water supply system of a large residential building or a group of houses. From the most general considerations, we can expect that such an object can be approximately described by a simpler model, replacing the sum of identical-looking models with different parameters with a single model of this type with averaged parameters, however, finding many parameters simply in the form of an average is, apparently, an unreasonable approach. It seems more reasonable to find the parameters by the approximating model by numerical optimization, in which the integral from the module or from the square of the deviation of the output signal of such a model from the output signal of the exact model is minimized when the test signal is applied. For linear models, the most reasonable test signal is a single step effect. This article tests this hypothesis and provides the results of this test.
{"title":"Investigation of linear models for control of water flow and temperature in a water supply system","authors":"A. Asset, Madina Mansurova, Vadim Zhmud, A. Kopesbaeva, Nurbolat Dzheksenbaev","doi":"10.11591/ijeecs.v35.i1.pp113-123","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp113-123","url":null,"abstract":"In some cases, the object model is a set of parallel models of the same general appearance, but with different parameters. The most common model is a model in the form of a serial connection of a first- or second-order filter and a delay link. An example is the water supply system of a large residential building or a group of houses. From the most general considerations, we can expect that such an object can be approximately described by a simpler model, replacing the sum of identical-looking models with different parameters with a single model of this type with averaged parameters, however, finding many parameters simply in the form of an average is, apparently, an unreasonable approach. It seems more reasonable to find the parameters by the approximating model by numerical optimization, in which the integral from the module or from the square of the deviation of the output signal of such a model from the output signal of the exact model is minimized when the test signal is applied. For linear models, the most reasonable test signal is a single step effect. This article tests this hypothesis and provides the results of this test.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp148-155
Sajjad Ahmed, N. Katiran, A. Joret, Shaharil Mohd Shah, Arslan Ahmed, Najwanisa Tusin
The paper describes a high-gain ultra-wideband (UWB) elliptical and circular slotted antipodal Vivaldi antenna (ECS-AVA) that is designed for through-wall detection systems. The antenna flares are loaded with elliptical and circular slots to improve the gain and broaden the bandwidth. To validate the efficacy of the designed antenna, a prototype of ECS-AVA is fabricated and subjected to measurements. The experimental findings suggest that the designed antenna can handle signals effectively across a range from 3.1 GHz to 10.6 GHz, as shown by its measured impedance bandwidth, with │S11│≤ -10 dB. The obtained measurements results are consistent with the results of the CST simulation. The proposed antenna exhibits improved radiation patterns in the UWB band with peak gain values ranging from 4.8 dB to 11.9 dB.
{"title":"High-gain UWB elliptical and circular slotted antipodal Vivaldi antenna for through wall detection","authors":"Sajjad Ahmed, N. Katiran, A. Joret, Shaharil Mohd Shah, Arslan Ahmed, Najwanisa Tusin","doi":"10.11591/ijeecs.v35.i1.pp148-155","DOIUrl":"https://doi.org/10.11591/ijeecs.v35.i1.pp148-155","url":null,"abstract":"The paper describes a high-gain ultra-wideband (UWB) elliptical and circular slotted antipodal Vivaldi antenna (ECS-AVA) that is designed for through-wall detection systems. The antenna flares are loaded with elliptical and circular slots to improve the gain and broaden the bandwidth. To validate the efficacy of the designed antenna, a prototype of ECS-AVA is fabricated and subjected to measurements. The experimental findings suggest that the designed antenna can handle signals effectively across a range from 3.1 GHz to 10.6 GHz, as shown by its measured impedance bandwidth, with │S11│≤ -10 dB. The obtained measurements results are consistent with the results of the CST simulation. The proposed antenna exhibits improved radiation patterns in the UWB band with peak gain values ranging from 4.8 dB to 11.9 dB.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141712270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.11591/ijeecs.v35.i1.pp1-13
Anil Kumar Dharavatu, Srinu Naik Ramavathu
The rapid increase of environmental impacts together global warming is conquered by substantial selection of electric-vehicles (EV’s) over the internal-combustion engine (ICE) vehicles. The replacement of these vehicles in transportation industry has led to reducing the running cost, ecological emissions, vehicle maintenance. The EV’s are operated by available battery energy and energized through utility-grid integrated EV charging stations. It is noted that, such charging stations may introduce power-quality issues, highly impacting the electric-grid due to presence of power electronic conversion devices in EV charging stations. The primary emphasis of power-quality impacts on electrical distribution grid are counteracted by employing active universal power-quality conditioner (AUPQC) device. The main role of AUPQC has been selected for mitigation of various PQ problems on both electric-grid side and charging station by using feasible control objective. In this work, a novel generalized voltage-current reference (GVCR) control objective has been proposed for extraction of fundamental reference voltage-current signals. The key findings are simple mathematical notations, no transformations, fast response, low dv/dt switch stress, low switching loss and maximum efficiency. The main goal is design, operation and performance of proposed GVCR controlled AUPQC device has been validated under integration of various EV chargers to electric-grid by using MATLAB/Simulink computing tool, simulation results are presented for analysis and interpretation.
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