Perhitungan Tegangan Sentuh Menggunakan Tahanan Kontak Kaki Dalam Sistem Pembumian Pada Gardu Induk Cikupa

Pada saat terjadi gangguan pada Gardu Induk,arus gangguan tanah yang mengalir di tempat gangguan maupun di tempat pembumian, Gardu Induk menimbulkan perbedaan tegangan di permukaan tanah yang dapat mengakibatkan terjadinya tegangan sentuh yang melampaui batas-batas keamanan bagi manusia yang berada di dalamnya saat terjadi gangguan.perlu dilakukan system pembumian yang sesuai dengan standar yang ada.Untuk menjamin keamanan dan keselamatan manusia terhadap bahaya tegangan lebih di Gardu Induk diperlukan system pembumian yang baik agar tidak melampaui tegangan sentuh yang diizinkan. Untuk menentukan nilai tegangan sentuh, diperlukan beberapa parameter. Parameter- parameter yang diperlukan diantaranya adalah besarnya arus gangguan tanah maksimum yang mungkin terjadi,luas Gardu Induk, tahanan jenis tanah, tahanan kontak kaki dan ukuran konduktor yang akan digunakan.Dari perhitungan diperoleh tegangan mesh atau tegangan sentuh maksimum yang sebenarnya (yang mungkin terjadi) sebesar 588,194 V (nilai ini masih berada di bawah nilai maksimum yang diizinkan,638V untuk ukuran berat badan 50 kg). Disamping itu dengan menggunakan tahanan kontak kaki sederhana dan tahanan kontak kaki efektif diperoleh tegangan sentuh masing_masing sebesar 469,44V dan 494,86V dengan kedalaman kisi_kisi pembumian 75 cm, jarak antara kedua kaki 70 cm dan perbandingan tahanan jenis tanah ( ρs/ ρ ) 100Ω . m dan kedua nilai ini masih berada pada batas yang diizinkan

A CHR-based Implementation of Known Arc-Consistency

In classical CLP(FD) systems, domains of variables are completely known at the beginning of the constraint propagation process. However, in systems interacting with an external environment, acquiring the whole domains of variables before the beginning of constraint propagation may cause waste of computation time, or even obsolescence of the acquired data at the time of use. For such cases, the Interactive Constraint Satisfaction Problem (ICSP) model has been proposed as an extension of the CSP model, to make it possible to start constraint propagation even when domains are not fully known, performing acquisition of domain elements only when necessary, and without the need for restarting the propagation after every acquisition. In this paper, we show how a solver for the two sorted CLP language, defined in previous work, to express ICSPs, has been implemented in the Constraint Handling Rules (CHR) language, a declarative language particularly suitable for high level implementation of constraint solvers.Comment: 22 pages, 2 figures, 1 table To appear in Theory and Practice of Logic Programming (TPLP

Lifted Variable Elimination for Probabilistic Logic Programming

Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP$^2$ for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.Comment: To appear in Theory and Practice of Logic Programming (TPLP). arXiv admin note: text overlap with arXiv:1402.0565 by other author

ANALISIS KESESUAIAN DAN DAYA DUKUNG KAWASAN WISATA PANTAI WARSEDULI, PIGEWA, DAN DULIBALA DI DESA ELOK, KABUPATEN ALOR

Abstrak: Pantai Werseduli, Pigewa dan Dulibala terletak di Desa Elok, Kecamatan Alor Timur,  Kabupaten  Alor Provinsi Nusa Tenggara Timur.  Kawasan ini memiliki daya tarik berupa pantai karang berpasir putih yang landai dengan pemandangan bawah laut yang menarik. Penelitian bertujuan untuk mengkaji kesesuaian kawasan untuk kegiatan wisata pantai kategori rekreasi pantai, berenang, berperahu dan menghitung daya dukung (carrying capacity).   Penelitian ini menggunakan  data primer dan data sekunder. Data primer berupa pengambilan data secara langsung di lapangan berdasarkan matriks indeks kesesuaian kawasan wisata pantai kategori rekreasi pantai, berenang dan berperahu sedangkan data sekunder diperoleh dari Dinas Perikanan dan Kelautan Kabupaten Alor, Dinas Pariwisata dan Kebudayaan Kabupaten Alor, Jurnal, Tesis, Buku, Wisatawan, Kepala Desa, Tokoh Masyarakat, Nelayan, Badan Pusat Statistik (BPS). Berdasarkan perhitungan daya dukung kawasan untuk kegiatan wisata pantai (Rekreasi, Berenang, Berperahu) pada stasiun 1 yang terletak di lokasi pantai Werseduli ini dapat menampung semua kegiatan wisata pantai sekitar 210 orang/hari. Stasiun 2 pada lokasi pantai Pigewa dapat menampung sekitar 117 orang/hari sedangkan pada lokasi stasiun ke tiga pantai Dulibala dapat menampung 237 orang/hari. Dengan data ini, dapat dijadikan rujukan pengelolaan pariwisata pantai Warseduli, Pigewa dan Dulibala kedepan. Abstract:  Elok Village, East Alor District, Alor Regency, East Nusa Tenggara Province is home to the beaches of Werseduli, Pigewa, and Dulibala. Sloping white sandy coral beaches with amazing underwater vistas are an attraction in this area. This study was completed in June of 2021. The study's goal was to assess the area's feasibility for beach tourism activities such as beach enjoyment, swimming, boating, and determining carrying capacity. This research makes use of both primary and secondary data. Primary data is collected in the field using an in situ data collection matrix based on the appropriateness matrix of coastal tourism zones, while secondary data is gathered through library research. According to an estimate of the area's carrying capacity for beach tourist activities (Recreation, Swimming, Boating), station 1, which is located in Werseduli, can handle 210 people per day for all beach tourism activities. Station 2 in Pigewa can accept roughly 117 people per day, whereas Station 3 in Dulibala can accommodate 237 people per day.

PELATIHAN TEKNIK MENGIKAT RUMPUT LAUT KEPADA PETANI RUMPUT LAUT SEBAGAI UPAYA MENINGKATKAN KEBERHASILAN PROSES PEMBUDIDAYAAN RUMPUT LAUT

Abstrak: Kegiatan pengabdian kepada masyarakat (PkM) ini, bertujuan untuk meningkatkan pemahaman masyarakat pembudidaya rumput laut Desa Blang Merang terkait teknik mengikat bibit rumput laut, agar tidak terlepas ke lingkungan dan proses budidaya dapat berhasil. Tali pengikat yang tidak kuat atau mudah terkelupas dapat meninggalkan serpihan pada tallus rumput laut dan mengurangi kualitas rumput laut sebagai komoditi ekspor. Metode pelaksanaan kegiatan pelatihan dan penyuluhan meliputi; (1) tahap persiapan; (2) tahap penerapan; (3) tahap evaluasi (mengamati proses aplikasi dilokasi budidaya oleh masyarakat pembudidaya). Kegiatan pelatihan dan penyuluhan teknik mengikat bibit rumput laut, dikatakan berhasil oleh sebab adanya perubahan metode atau pola mengikat rumput laut oleh masyarakat pembudidaya. Hal ini tergambar dari kemampuan ketrampilan masyarakat pembudidaya rumput laut Desa Blang Merang yang sudah menerapkannya, seusai kegiatan pelatihan dan penyuluhan teknik mengikat bibit rumput laut dilaksanakan.Abstract:  This community service activity (PkM) aims to improve the understanding of the seaweed farming community of Blang Merang Village related to seaweed seedling binding techniques, so that it does not escape to the environment and the cultivation process can be successful. Fastening straps that are not strong or easily chipped can leave flakes on seaweed tallus and reduce the quality of seaweed as an export commodity. Methods of implementation of training and counseling activities include; (1) the preparatory stage; (2) the implementation stage; (3) the evaluation stage (observing the application process at the location of cultivation by the cultivating community). Training activities and counseling techniques binding seaweed seedlings are said to be successful because of changes in methods or patterns of binding seaweed by the farming community. This is illustrated from the skills of the seaweed farming community of Blang Merang Village who have implemented it, after training activities and counseling techniques binding seaweed seedlings were implemented

Exploiting Parameters Learning for Hyper-parameters Optimization in Deep Neural Networks

In the last years, the Hyper-parameter Optimization (HPO) research field has gained more and more attention. Many works have focused on finding the best combination of the Deep Neural Network's (DNN's) hyper-parameters (HPs) or architecture. The state-of-the-art algorithm in terms of HPO is Bayesian Optimization (BO). This is because it keeps track of past results obtained during the optimization and uses this experience to build a probabilistic model mapping HPs to a probability density of the objective function. BO builds a surrogate probabilistic model of the objective function, finds the HPs values that perform best on the surrogate model and updates it with new results. In this work, a system was developed, called Symbolic DNN-Tuner which logically evaluates the results obtained from the training and the validation phase and, by applying symbolic tuning rules, fixes the network architecture, and its HPs, therefore improving performance. Symbolic DNN-Tuner improve BO applied to DNN by adding an analysis of the results of the network on training and validation sets. This analysis is performed by exploiting rule-based programming, and in particular by using Probabilistic Logic Programming (PLP)

Exploiting CNN’s visual explanations to drive anomaly detection

Nowadays, deep learning is a key technology for many applications in the industrial area such as anomaly detection. The role of Machine Learning (ML) in this field relies on the ability of training a network to learn to inspect images to determine the presence or not of anomalies. Frequently, in Industry 4.0 w.r.t. the anomaly detection task, the images to be analyzed are not optimal, since they contain edges or areas, that are not of interest which could lead the network astray. Thus, this study aims at identifying a systematic way to train a neural network to make it able to focus only on the area of interest. The study is based on the definition of a loss to be applied in the training phase of the network that, using masks, gives higher weight to the anomalies identified within the area of interest. The idea is to add an Overlap Coefficient to the standard cross-entropy. In this way, the more the identified anomaly is outside the Area of Interest (AOI) the greater is the loss. We call the resulting loss Cross-Entropy Overlap Distance (CEOD). The advantage of adding the masks in the training phase is that the network is forced to learn and recognize defects only in the area circumscribed by the mask. The added benefit is that, during inference, these masks will no longer be needed. Therefore, there is no difference, in terms of execution times, between a standard Convolutional Neural Network (CNN) and a network trained with this loss. In some applications, the masks themselves are determined at run-time through a trained segmentation network, as we have done for instance in the "Machine learning for visual inspection and quality control" project, funded by the MISE Competence Center Bi-REX

Penentuan Jenis Penyangga Menggunakan Kombinasi Metode RMR, Numerik dan Probabilitas di Crosscut 12 access PT. Cibaliung Sumber Daya

Pemilihan jenis penyangga dalam memastikan kestabilan lubang bukaan menjadi salah satu yang harus diperhatikan dengan serius. Hal ini bisa berdampak pada ketidakstabilan dan berakhir dengan terjadinya runtuhan lubang bukaan tersebut. Oleh karena itu, perlu dilakukan analisis terkait yang tentunya memerlukan metode yang mampu mengakomodasi berbagai variabel guna mendekati kondisi sebenarnya. Metode probabilitas seperti yang dikemukakan oleh Arif (2016), mampu mengakomodasi setiap variasi data karakteristik dari parameter yang hasilnya dinyatakan dalam bentuk probabilitas kelongsoran (PK). Dalam penelitian ini, digunakan kombinasi metode RMR’89, Numerik dan Probabilitas yang menjadi suatu rangkaian proses analisis guna saling melengkapi dan mendukung untuk mencapai hasil yang lebih akurat. Selain itu kriteria keruntuhan yang digunakan yaitu mohr coulomb dengan nilai FK minimal sebesar 1,5 juga analsisis monte carlo untuk metode probabilitas.Hasil penelitian menunjukkan nilai RMR sebesar 42 (fair rock) dan jenis penyangga yang direkomendasi oleh Bieniawski (1989) yaitu rockbolt (panjang 4 m, spasi 1,5 m), shotcrete tebal 100 mm dan wiremesh. Namun saat dianalisis dengan metode numerik dan probabilitas, jenis penyangga tersebut tidak efektif karena diperoleh nilai FK rata – ratanya 1,21 dengan probabilitas keruntuhan 89,59 %. Untuk itu dilakukan modifikasi jenis penyangga yang akhirnya diperoleh jenis penyangga yang sesuai kriteria nilai FK dan probabilitas lebih kecil yaitu kombinasi rockbolt (panjang 4 m, spasi 1,5 m), shotcrete tebal 100 mm, wiremesh dan H-Beam. Adapun nilai FK rata – ratanya 1,6 dengan probabilitas keruntuhan 33,07 %.Kata Kunci : Penyangga, RMR, Numerik, Probabilita