LAMPU LALU LINTAS UNTUK PENYEBERANG JALAN

Traffic lights for pedestrians are necessary to provide security and comfort for pedestrians and vehicle drivers in order to avoid accidents and traffic jams. During the design of the circuit, component selection analysis was performed for assembling the controller. The analysis showed that the industrial timer has the ease of setting intervals and ease of maintenance controller in the case of damage. The testing on the design showed that, aftercrossing demand button was pressed, the traffic lights worked well in one cycle to allow pedestrians and further provides the opportunity for passing vehicles until the crossing demand button was pressed again by a pedestrian.Lampu lalu lintas untuk penyeberang jalan, merupakan hal yang dibutuhkan untuk memberikan rasa aman dan nyaman bagi pejalan kaki maupun pengemudi kendaraan untuk menghindari terjadinya kecelakaan dan kemacetan lalu lintas. Dalam perancangan rangkaian dilakukan analisis pemilihan komponen untuk merakit kontroler. Analisis menunjukkan bahwa Timer industri memiliki kemudahan dalam melakukan pengaturanselang waktu dan kemudahan dalam perawatan kontroler jika terjadi kerusakan. Pengujian atas perancangan menunjukkan, setelah penekanan tombol permintaan menyeberang, lampulalu lintas bekerja baik sesuai satu siklus untuk memberi kesempatan penyeberang jalan, dan selanjutnya memberi kesempatan untuk kendaraan melintas sampai tombol permintaanmenyeberang ditekan oleh penyeberang selanjutnya

Rancang Bangun Sistem Kamera Pengawas dengan Pengenalan Wajah untuk Keamanan Berbasis Blynk Legacy

Covid-19 pandemic that has occurred since the beginning of 2020 has brought down all aspects of the country, starting from community activities to the economy. This has an impact on increasing the number of crimes committed by the community such as theft, robbery or other crimes. In this study, a room security system is proposed that uses a surveillance camera with a face recognition ability that records the face image of an intruder and records events as evidence of an intrusion. This system sends information quickly and automatically to the Android application user if an intruder who the camera doesn't recognize enters his house. The smartphone application user can control camera movements inside the house to monitor the movement of intruders and record the incident. This system uses 5 ESP32-CAM cameras. One camera is used to recognize and record the intruder's face image placed in front of the house and four cameras as surveillance and face recognition cameras are placed inside of the house. Each camera is driven by a servo motor controlled by a ESP8266 microcontroller. From the test results it is known that the maximum distance that the cameras still recognize the face image of an intruder or the home owner's face image is 2 meters when the light is bright. When it is dim, the camera in front of the house recognizes the face images up to 0.5 meters while the cameras inside of the house recognize the face images up to 1 meter. The average delay time for sending data from the camera system to application user is 201 ms to 617 ms.  

State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems

The Solar Dryer Dome (SDD), an independent energy system equipped with Artificial Intelligence to support the drying process, has been developed. However, inaccurate state-of-charge (SOC) predictions in each battery cell resulted in the vulnerability of the battery to over-charging and over-discharging, which accelerated the battery performance degradation. This research aims to develop an accurate neural network model for predicting the SOC of battery-cell level. The model aims to maintain the battery cell balance under dynamic load applications. It is accompanied by a developed dashboard to monitor and provide crucial information for early maintenance of the battery in the SDD. The results show that the neural network estimates the SOC with the lowest MAE of 0.175, followed by the Random Forest and support vector machine methods with MAE of 0.223 and 0.259, respectively. A dashboard was developed to help farmers monitor batteries efficiently. This research contributes to battery-cell level SOC prediction and the dashboard for battery status monitoring. Doi: 10.28991/ESJ-2023-07-03-02 Full Text: PD

Constructing tag ontology from folksonomy based on WordNet

With the emergence of Web 2.0, Web users can classify Web items of their interest by using tags. Tags reflect users’ understanding to the items collected in each tag. Exploring user tagging behavior provides a promising way to understand users’ information needs. However, free and relatively uncontrolled vocabulary has its drawback in terms of lack of standardization and semantic ambiguity. Moreover, the relationships among tags have not been explored even there exist rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach to construct tag ontology based on the widely used general ontology WordNet to capture the semantics and the structural relationships of tags. Ambiguity of tags is a challenging problem to deal with in order to construct high quality tag ontology. We propose strategies to find the semantic meanings of tags and a strategy to disambiguate the semantics of tags based on the opinion of WordNet lexicographers. In order to evaluate the usefulness of the constructed tag ontology, in this paper we apply the extracted tag ontology in a tag recommendation experiment. We believe this is the first application of tag ontology for recommendation making. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved

Learning personalized tag ontology from user tagging information

The cross-sections of the Social Web and the Semantic Web has put folksonomy in the spot light for its potential in overcoming knowledge acquisition bottleneck and providing insight for "wisdom of the crowds". Folksonomy which comes as the results of collaborative tagging activities has provided insight into user's understanding about Web resources which might be useful for searching and organizing purposes. However, collaborative tagging vocabulary poses some challenges since tags are freely chosen by users and may exhibit synonymy and polysemy problem. In order to overcome these challenges and boost the potential of folksonomy as emergence semantics we propose to consolidate the diverse vocabulary into a consolidated entities and concepts. We propose to extract a tag ontology by ontology learning process to represent the semantics of a tagging community. This paper presents a novel approach to learn the ontology based on the widely used lexical database WordNet. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. We provide empirical evaluations by using the semantic information contained in the ontology in a tag recommendation experiment. The results show that by using the semantic relationships on the ontology the accuracy of the tag recommender has been improved

Personalization in tag ontology learning for recommendation making

Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. One of the most popular web personalization systems is recommender systems. In recommender systems choosing user information that can be used to profile users is very crucial for user profiling. In Web 2.0, one facility that can help users organize Web resources of their interest is user tagging systems. Exploring user tagging behavior provides a promising way for understanding users’ information needs since tags are given directly by users. However, free and relatively uncontrolled vocabulary makes the user self-defined tags lack of standardization and semantic ambiguity. Also, the relationships among tags need to be explored since there are rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach for learning tag ontology based on the widely used lexical database WordNet for capturing the semantics and the structural relationships of tags. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. To personalize further, clustering of users is performed to generate a more accurate ontology for a particular group of users. In order to evaluate the usefulness of the tag ontology, we use the tag ontology in a pilot tag recommendation experiment for improving the recommendation performance by exploiting the semantic information in the tag ontology. The initial result shows that the personalized information has improved the accuracy of the tag recommendation

A combined method for mitigating sparsity problem in tag recommendation

Tag recommendation is a specific recommendation task for recommending metadata (tag) for a web resource (item) during user annotation process. In this context, sparsity problem refers to situation where tags need to be produced for items with few annotations or for user who tags few items. Most of the state of the art approaches in tag recommendation are rarely evaluated or perform poorly under this situation. This paper presents a combined method for mitigating sparsity problem in tag recommendation by mainly expanding and ranking candidate tags based on similar items’ tags and existing tag ontology. We evaluated the approach on two public social bookmarking datasets. The experiment results show better accuracy for recommendation in sparsity situation over several state of the art methods

Ontology learning from user tagging for tag recommendation making

Recently, user tagging systems have grown in popularity on the web. The tagging process is quite simple for ordinary users, which contributes to its popularity. However, free vocabulary has lack of standardization and semantic ambiguity. It is possible to capture the semantics from user tagging into some form of ontology, but the application of the resulted ontology for recommendation making has not been that flourishing. In this paper we discuss our approach to learn domain ontology from user tagging information and apply the extracted tag ontology in a pilot tag recommendation experiment. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved